Note
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Comparison between EM and gradient ascent for tracking a simulated mouse
The code below compares the expectation maximization (EM) and gradient ascent algorithm for tracking a simulated mouse.
import numpy as np
import torch
import plotly.graph_objs as go
import ssm.tracking.utils
import ssm.simulation
import ssm.learning
Simulation
We will simulate a linear dynamical system for tracking. The parameters of this model are highlighted in red.
\[\begin{split}\begin{equation}
\begin{alignedat}{4}
& x_n&&=A x_{n-1}+w_n&&\quad with\quad&&w_n\sim\mathcal{N}(0,\textcolor{red}{\sigma_a^2}Q_e),&&\quad x_n\in\mathbb{R}^6&&\label{eq:ssm}\\
& y_n&&=C x_n+v_n&&\quad with\quad&&v_n\sim\mathcal{N}(0,R),&&\quad y_n\in\mathbb{R}^2,&&\quad R=\left[\begin{array}{c,c}
\textcolor{red}{\sigma_x^2}&0\\
0&\textcolor{red}{\sigma_y^2}
\end{array}\right]\\
& x_0&&\in\mathcal{N}(\textcolor{red}{m_0},\textcolor{red}{V_0})&& &&
\end{alignedat}
\end{equation}\end{split}\]
Define simulation parameters
pos_x0 = 0.0
pos_y0 = 0.0
vel_x0 = 0.0
vel_y0 = 0.0
acc_x0 = 0.0
acc_y0 = 0.0
dt = 1e-1
num_pos = 500
sigma_a_true = 0.1
sigma_x = 5e-0
sigma_y = 5e-0
sqrt_diag_V0_value = 1e-3
B, _, Qe, Z, R = ssm.tracking.utils.getLDSmatricesForKinematics_np(
dt=dt, sigma_a=np.nan, pos_x_R_std=sigma_x, pos_y_R_std=sigma_y)
Q_true = Qe*sigma_a_true**2
sqrt_diag_R = np.array([sigma_x, sigma_y])
m0 = np.array([pos_x0, vel_x0, acc_x0, pos_y0, vel_y0, acc_y0],
dtype=np.double)
sqrt_diag_V0 = np.ones(6)*sqrt_diag_V0_value
V0 = np.diag(sqrt_diag_V0**2)
Perform simulation
Code for ssm.simulation.simulateLDS
x0, x, y = ssm.simulation.simulateLDS(T=num_pos, B=B, Q=Q_true, Z=Z, R=R,
m0=m0, V0=V0)
Plot simulated state and measurement positions
fig = go.Figure()
trace_x = go.Scatter(x=x[0, :], y=x[3, :], mode="lines+markers",
showlegend=True, name="pos. state mean")
trace_y = go.Scatter(x=y[0, :], y=y[1, :], mode="lines+markers",
showlegend=True, name="measurement", opacity=0.3)
trace_start = go.Scatter(x=[x0[0]], y=[x0[3]], mode="markers",
text="initial state", marker={"size": 7},
showlegend=False)
fig.add_trace(trace_x)
fig.add_trace(trace_y)
fig.add_trace(trace_start)
fig.update_layout(xaxis_title="horizontal direction",
yaxis_title="vertical direction")
fig
Estimation of the \(\sigma_a^2\) parameter only
sigma_a0 = 0.25
ga_vars_to_estimate = {"sigma_a": True,
"pos_x_R_std": False, "pos_y_R_std": False,
"m0": False, "sqrt_diag_V0": False}
em_vars_to_estimate = {"sigma_a": True, "R": False,
"m0": False, "V0": False}
em_max_iter = 200
lbfgs_max_iter = 2
lbfgs_n_epochs = 100
lbfgs_tolerance_grad = 1e-9
lbfgs_tolerance_change = 1e-7
Grid search
sigma_a_min = 0.001
sigma_a_max = 2.0
sigma_a_step = 0.005
sqrt_noise_intensities = np.arange(sigma_a_min, sigma_a_max, sigma_a_step)
gs_log_likes = np.empty(len(sqrt_noise_intensities))
for i, sigma_a in enumerate(sqrt_noise_intensities):
Q_gs = Qe * sigma_a**2
filterRes = ssm.inference.filterLDS_SS_withMissingValues_np(
y=y, B=B, Q=Q_gs, m0=m0, V0=V0, Z=Z, R=R)
gs_log_likes[i] = filterRes["logLike"]
print(f"log likelihood for sigma_a={sigma_a:.04f}: {gs_log_likes[i]}")
argmax = np.argmax(gs_log_likes)
gs_max_ll = gs_log_likes[argmax]
gs_max_sigma_a = sqrt_noise_intensities[argmax]
print(f"max log likelihood: {gs_max_ll}, "
f"max sigma_a: {gs_max_sigma_a}")
log likelihood for sigma_a=0.0010: -17524.006211816642
log likelihood for sigma_a=0.0060: -3640.846921388277
log likelihood for sigma_a=0.0110: -3291.830915806264
log likelihood for sigma_a=0.0160: -3196.752434151069
log likelihood for sigma_a=0.0210: -3155.373427345535
log likelihood for sigma_a=0.0260: -3133.4453426806813
log likelihood for sigma_a=0.0310: -3120.4263883485323
log likelihood for sigma_a=0.0360: -3112.09384595978
log likelihood for sigma_a=0.0410: -3106.475433200822
log likelihood for sigma_a=0.0460: -3102.546589004807
log likelihood for sigma_a=0.0510: -3099.7302279170667
log likelihood for sigma_a=0.0560: -3097.679607838207
log likelihood for sigma_a=0.0610: -3096.1750715283
log likelihood for sigma_a=0.0660: -3095.071240989042
log likelihood for sigma_a=0.0710: -3094.2683317005276
log likelihood for sigma_a=0.0760: -3093.6957426484414
log likelihood for sigma_a=0.0810: -3093.302236370567
log likelihood for sigma_a=0.0860: -3093.0498254529107
log likelihood for sigma_a=0.0910: -3092.9098316984037
log likelihood for sigma_a=0.0960: -3092.8602669458705
log likelihood for sigma_a=0.1010: -3092.884045041279
log likelihood for sigma_a=0.1060: -3092.9677323201695
log likelihood for sigma_a=0.1110: -3093.100656419926
log likelihood for sigma_a=0.1160: -3093.274259252578
log likelihood for sigma_a=0.1210: -3093.4816198843005
log likelihood for sigma_a=0.1260: -3093.7170978723534
log likelihood for sigma_a=0.1310: -3093.9760634198788
log likelihood for sigma_a=0.1360: -3094.2546910204182
log likelihood for sigma_a=0.1410: -3094.5498001355354
log likelihood for sigma_a=0.1460: -3094.85873111573
log likelihood for sigma_a=0.1510: -3095.179247802405
log likelihood for sigma_a=0.1560: -3095.509460515195
log likelihood for sigma_a=0.1610: -3095.8477647448417
log likelihood for sigma_a=0.1660: -3096.1927920384005
log likelihood for sigma_a=0.1710: -3096.5433704152874
log likelihood for sigma_a=0.1760: -3096.8984922822847
log likelihood for sigma_a=0.1810: -3097.2572882843447
log likelihood for sigma_a=0.1860: -3097.6190058807138
log likelihood for sigma_a=0.1910: -3097.982991702681
log likelihood for sigma_a=0.1960: -3098.348676954014
log likelihood for sigma_a=0.2010: -3098.715565271049
log likelihood for sigma_a=0.2060: -3099.083222580725
log likelihood for sigma_a=0.2110: -3099.4512685894265
log likelihood for sigma_a=0.2160: -3099.819369608426
log likelihood for sigma_a=0.2210: -3100.1872324800383
log likelihood for sigma_a=0.2260: -3100.554599414133
log likelihood for sigma_a=0.2310: -3100.9212435803347
log likelihood for sigma_a=0.2360: -3101.286965330829
log likelihood for sigma_a=0.2410: -3101.651588950559
log likelihood for sigma_a=0.2460: -3102.01495985161
log likelihood for sigma_a=0.2510: -3102.3769421419247
log likelihood for sigma_a=0.2560: -3102.737416511191
log likelihood for sigma_a=0.2610: -3103.096278386564
log likelihood for sigma_a=0.2660: -3103.4534363190946
log likelihood for sigma_a=0.2710: -3103.8088105669594
log likelihood for sigma_a=0.2760: -3104.1623318488523
log likelihood for sigma_a=0.2810: -3104.5139402431105
log likelihood for sigma_a=0.2860: -3104.86358421428
log likelihood for sigma_a=0.2910: -3105.211219748926
log likelihood for sigma_a=0.2960: -3105.5568095877493
log likelihood for sigma_a=0.3010: -3105.9003225410383
log likelihood for sigma_a=0.3060: -3106.241732877554
log likelihood for sigma_a=0.3110: -3106.5810197781675
log likelihood for sigma_a=0.3160: -3106.918166845554
log likelihood for sigma_a=0.3210: -3107.2531616645697
log likelihood for sigma_a=0.3260: -3107.5859954067696
log likelihood for sigma_a=0.3310: -3107.916662474369
log likelihood for sigma_a=0.3360: -3108.2451601789207
log likelihood for sigma_a=0.3410: -3108.571488451644
log likelihood for sigma_a=0.3460: -3108.8956495814123
log likelihood for sigma_a=0.3510: -3109.217647977278
log likelihood for sigma_a=0.3560: -3109.5374899540034
log likelihood for sigma_a=0.3610: -3109.8551835372
log likelihood for sigma_a=0.3660: -3110.1707382865516
log likelihood for sigma_a=0.3710: -3110.484165135336
log likelihood for sigma_a=0.3760: -3110.7954762445565
log likelihood for sigma_a=0.3810: -3111.1046848703304
log likelihood for sigma_a=0.3860: -3111.4118052430717
log likelihood for sigma_a=0.3910: -3111.7168524579706
log likelihood for sigma_a=0.3960: -3112.0198423743886
log likelihood for sigma_a=0.4010: -3112.3207915251874
log likelihood for sigma_a=0.4060: -3112.6197170334326
log likelihood for sigma_a=0.4110: -3112.9166365366746
log likelihood for sigma_a=0.4160: -3113.211568117868
log likelihood for sigma_a=0.4210: -3113.504530242711
log likelihood for sigma_a=0.4260: -3113.7955417017324
log likelihood for sigma_a=0.4310: -3114.084621558469
log likelihood for sigma_a=0.4360: -3114.371789101378
log likelihood for sigma_a=0.4410: -3114.6570638007083
log likelihood for sigma_a=0.4460: -3114.940465268517
log likelihood for sigma_a=0.4510: -3115.2220132228163
log likelihood for sigma_a=0.4560: -3115.5017274543816
log likelihood for sigma_a=0.4610: -3115.779627797463
log likelihood for sigma_a=0.4660: -3116.0557341019335
log likelihood for sigma_a=0.4710: -3116.3300662088977
log likelihood for sigma_a=0.4760: -3116.6026439282
log likelihood for sigma_a=0.4810: -3116.8734870180033
log likelihood for sigma_a=0.4860: -3117.1426151665055
log likelihood for sigma_a=0.4910: -3117.41004797515
log likelihood for sigma_a=0.4960: -3117.675804943332
log likelihood for sigma_a=0.5010: -3117.939905455481
log likelihood for sigma_a=0.5060: -3118.2023687679325
log likelihood for sigma_a=0.5110: -3118.4632139986898
log likelihood for sigma_a=0.5160: -3118.722460117308
log likelihood for sigma_a=0.5210: -3118.980125936
log likelihood for sigma_a=0.5260: -3119.2362301021303
log likelihood for sigma_a=0.5310: -3119.490791091094
log likelihood for sigma_a=0.5360: -3119.7438272000736
log likelihood for sigma_a=0.5410: -3119.995356543248
log likelihood for sigma_a=0.5460: -3120.2453970465112
log likelihood for sigma_a=0.5510: -3120.4939664437798
log likelihood for sigma_a=0.5560: -3120.7410822735264
log likelihood for sigma_a=0.5610: -3120.986761875961
log likelihood for sigma_a=0.5660: -3121.2310223903387
log likelihood for sigma_a=0.5710: -3121.473880753208
log likelihood for sigma_a=0.5760: -3121.7153536971323
log likelihood for sigma_a=0.5810: -3121.955457748845
log likelihood for sigma_a=0.5860: -3122.194209229003
log likelihood for sigma_a=0.5910: -3122.4316242515633
log likelihood for sigma_a=0.5960: -3122.6677187233618
log likelihood for sigma_a=0.6010: -3122.902508344384
log likelihood for sigma_a=0.6060: -3123.1360086081663
log likelihood for sigma_a=0.6110: -3123.368234801766
log likelihood for sigma_a=0.6160: -3123.599202007291
log likelihood for sigma_a=0.6210: -3123.828925102109
log likelihood for sigma_a=0.6260: -3124.057418760245
log likelihood for sigma_a=0.6310: -3124.2846974536005
log likelihood for sigma_a=0.6360: -3124.510775453142
log likelihood for sigma_a=0.6410: -3124.7356668306797
log likelihood for sigma_a=0.6460: -3124.959385460099
log likelihood for sigma_a=0.6510: -3125.1819450192725
log likelihood for sigma_a=0.6560: -3125.4033589919845
log likelihood for sigma_a=0.6610: -3125.6236406693315
log likelihood for sigma_a=0.6660: -3125.8428031522253
log likelihood for sigma_a=0.6710: -3126.060859352835
log likelihood for sigma_a=0.6760: -3126.277821997029
log likelihood for sigma_a=0.6810: -3126.4937036264114
log likelihood for sigma_a=0.6860: -3126.7085166002594
log likelihood for sigma_a=0.6910: -3126.922273098009
log likelihood for sigma_a=0.6960: -3127.1349851210866
log likelihood for sigma_a=0.7010: -3127.3466644955274
log likelihood for sigma_a=0.7060: -3127.5573228739313
log likelihood for sigma_a=0.7110: -3127.76697173789
log likelihood for sigma_a=0.7160: -3127.975622400215
log likelihood for sigma_a=0.7210: -3128.1832860070817
log likelihood for sigma_a=0.7260: -3128.3899735405716
log likelihood for sigma_a=0.7310: -3128.595695820962
log likelihood for sigma_a=0.7360: -3128.800463508658
log likelihood for sigma_a=0.7410: -3129.0042871068918
log likelihood for sigma_a=0.7460: -3129.20717696383
log likelihood for sigma_a=0.7510: -3129.409143274691
log likelihood for sigma_a=0.7560: -3129.610196084352
log likelihood for sigma_a=0.7610: -3129.8103452892587
log likelihood for sigma_a=0.7660: -3130.009600639905
log likelihood for sigma_a=0.7710: -3130.207971742856
log likelihood for sigma_a=0.7760: -3130.4054680630966
log likelihood for sigma_a=0.7810: -3130.602098926193
log likelihood for sigma_a=0.7860: -3130.7978735204465
log likelihood for sigma_a=0.7910: -3130.992800898763
log likelihood for sigma_a=0.7960: -3131.1868899813626
log likelihood for sigma_a=0.8010: -3131.3801495574444
log likelihood for sigma_a=0.8060: -3131.5725882872393
log likelihood for sigma_a=0.8110: -3131.764214704255
log likelihood for sigma_a=0.8160: -3131.9550372171684
log likelihood for sigma_a=0.8210: -3132.145064112163
log likelihood for sigma_a=0.8260: -3132.334303554328
log likelihood for sigma_a=0.8310: -3132.522763590149
log likelihood for sigma_a=0.8360: -3132.710452149059
log likelihood for sigma_a=0.8410: -3132.897377045551
log likelihood for sigma_a=0.8460: -3133.0835459812065
log likelihood for sigma_a=0.8510: -3133.268966546065
log likelihood for sigma_a=0.8560: -3133.453646220819
log likelihood for sigma_a=0.8610: -3133.637592378742
log likelihood for sigma_a=0.8660: -3133.820812286835
log likelihood for sigma_a=0.8710: -3134.003313108546
log likelihood for sigma_a=0.8760: -3134.1851019044793
log likelihood for sigma_a=0.8810: -3134.3661856346025
log likelihood for sigma_a=0.8860: -3134.546571160204
log likelihood for sigma_a=0.8910: -3134.726265244884
log likelihood for sigma_a=0.8960: -3134.9052745565177
log likelihood for sigma_a=0.9010: -3135.0836056688418
log likelihood for sigma_a=0.9060: -3135.2612650631795
log likelihood for sigma_a=0.9110: -3135.438259129416
log likelihood for sigma_a=0.9160: -3135.6145941683453
log likelihood for sigma_a=0.9210: -3135.7902763924308
log likelihood for sigma_a=0.9260: -3135.9653119274735
log likelihood for sigma_a=0.9310: -3136.139706814489
log likelihood for sigma_a=0.9360: -3136.3134670105146
log likelihood for sigma_a=0.9410: -3136.4865983903446
log likelihood for sigma_a=0.9460: -3136.6591067477034
log likelihood for sigma_a=0.9510: -3136.8309977968747
log likelihood for sigma_a=0.9560: -3137.002277173906
log likelihood for sigma_a=0.9610: -3137.1729504376835
log likelihood for sigma_a=0.9660: -3137.3430230714557
log likelihood for sigma_a=0.9710: -3137.5125004842166
log likelihood for sigma_a=0.9760: -3137.681388011606
log likelihood for sigma_a=0.9810: -3137.849690917154
log likelihood for sigma_a=0.9860: -3138.0174143938434
log likelihood for sigma_a=0.9910: -3138.1845635650106
log likelihood for sigma_a=0.9960: -3138.351143485465
log likelihood for sigma_a=1.0010: -3138.5171591426383
log likelihood for sigma_a=1.0060: -3138.6826154577534
log likelihood for sigma_a=1.0110: -3138.8475172870117
log likelihood for sigma_a=1.0160: -3139.0118694224902
log likelihood for sigma_a=1.0210: -3139.175676593271
log likelihood for sigma_a=1.0260: -3139.338943466515
log likelihood for sigma_a=1.0310: -3139.501674648444
log likelihood for sigma_a=1.0360: -3139.6638746850485
log likelihood for sigma_a=1.0410: -3139.825548063867
log likelihood for sigma_a=1.0460: -3139.9866992139396
log likelihood for sigma_a=1.0510: -3140.147332507596
log likelihood for sigma_a=1.0560: -3140.3074522608413
log likelihood for sigma_a=1.0610: -3140.467062734437
log likelihood for sigma_a=1.0660: -3140.626168134961
log likelihood for sigma_a=1.0710: -3140.7847726155696
log likelihood for sigma_a=1.0760: -3140.942880276695
log likelihood for sigma_a=1.0810: -3141.100495166916
log likelihood for sigma_a=1.0860: -3141.2576212843146
log likelihood for sigma_a=1.0910: -3141.41426257665
log likelihood for sigma_a=1.0960: -3141.5704229421813
log likelihood for sigma_a=1.1010: -3141.726106231161
log likelihood for sigma_a=1.1060: -3141.881316245748
log likelihood for sigma_a=1.1110: -3142.0360567412718
log likelihood for sigma_a=1.1160: -3142.19033142691
log likelihood for sigma_a=1.1210: -3142.3441439662
log likelihood for sigma_a=1.1260: -3142.4974979779945
log likelihood for sigma_a=1.1310: -3142.6503970372187
log likelihood for sigma_a=1.1360: -3142.802844675244
log likelihood for sigma_a=1.1410: -3142.954844380859
log likelihood for sigma_a=1.1460: -3143.106399600888
log likelihood for sigma_a=1.1510: -3143.2575137406675
log likelihood for sigma_a=1.1560: -3143.408190165006
log likelihood for sigma_a=1.1610: -3143.558432198445
log likelihood for sigma_a=1.1660: -3143.708243126182
log likelihood for sigma_a=1.1710: -3143.857626194686
log likelihood for sigma_a=1.1760: -3144.006584611931
log likelihood for sigma_a=1.1810: -3144.155121548418
log likelihood for sigma_a=1.1860: -3144.303240137585
log likelihood for sigma_a=1.1910: -3144.450943476192
log likelihood for sigma_a=1.1960: -3144.5982346252163
log likelihood for sigma_a=1.2010: -3144.745116610331
log likelihood for sigma_a=1.2060: -3144.8915924219777
log likelihood for sigma_a=1.2110: -3145.0376650165563
log likelihood for sigma_a=1.2160: -3145.1833373166783
log likelihood for sigma_a=1.2210: -3145.328612211598
log likelihood for sigma_a=1.2260: -3145.4734925575435
log likelihood for sigma_a=1.2310: -3145.6179811788174
log likelihood for sigma_a=1.2360: -3145.762080867574
log likelihood for sigma_a=1.2410: -3145.90579438491
log likelihood for sigma_a=1.2460: -3146.0491244606033
log likelihood for sigma_a=1.2510: -3146.1920737945998
log likelihood for sigma_a=1.2560: -3146.3346450562367
log likelihood for sigma_a=1.2610: -3146.476840885843
log likelihood for sigma_a=1.2660: -3146.6186638942077
log likelihood for sigma_a=1.2710: -3146.760116663932
log likelihood for sigma_a=1.2760: -3146.90120174881
log likelihood for sigma_a=1.2810: -3147.0419216752193
log likelihood for sigma_a=1.2860: -3147.1822789419807
log likelihood for sigma_a=1.2910: -3147.322276020674
log likelihood for sigma_a=1.2960: -3147.461915356639
log likelihood for sigma_a=1.3010: -3147.6011993684215
log likelihood for sigma_a=1.3060: -3147.7401304492473
log likelihood for sigma_a=1.3110: -3147.8787109663235
log likelihood for sigma_a=1.3160: -3148.0169432619537
log likelihood for sigma_a=1.3210: -3148.1548296536353
log likelihood for sigma_a=1.3260: -3148.2923724344046
log likelihood for sigma_a=1.3310: -3148.429573873124
log likelihood for sigma_a=1.3360: -3148.5664362150774
log likelihood for sigma_a=1.3410: -3148.7029616818654
log likelihood for sigma_a=1.3460: -3148.8391524721696
log likelihood for sigma_a=1.3510: -3148.9750107619147
log likelihood for sigma_a=1.3560: -3149.110538704403
log likelihood for sigma_a=1.3610: -3149.2457384309296
log likelihood for sigma_a=1.3660: -3149.3806120510685
log likelihood for sigma_a=1.3710: -3149.5151616524845
log likelihood for sigma_a=1.3760: -3149.6493893018937
log likelihood for sigma_a=1.3810: -3149.783297045161
log likelihood for sigma_a=1.3860: -3149.9168869070536
log likelihood for sigma_a=1.3910: -3150.050160892363
log likelihood for sigma_a=1.3960: -3150.1831209856846
log likelihood for sigma_a=1.4010: -3150.315769151714
log likelihood for sigma_a=1.4060: -3150.4481073355187
log likelihood for sigma_a=1.4110: -3150.5801374629045
log likelihood for sigma_a=1.4160: -3150.711861440862
log likelihood for sigma_a=1.4210: -3150.8432811573575
log likelihood for sigma_a=1.4260: -3150.974398481875
log likelihood for sigma_a=1.4310: -3151.1052152654443
log likelihood for sigma_a=1.4360: -3151.2357333414534
log likelihood for sigma_a=1.4410: -3151.3659545251994
log likelihood for sigma_a=1.4460: -3151.4958806143313
log likelihood for sigma_a=1.4510: -3151.6255133892455
log likelihood for sigma_a=1.4560: -3151.754854613377
log likelihood for sigma_a=1.4610: -3151.8839060331948
log likelihood for sigma_a=1.4660: -3152.0126693781904
log likelihood for sigma_a=1.4710: -3152.1411463618724
log likelihood for sigma_a=1.4760: -3152.2693386810647
log likelihood for sigma_a=1.4810: -3152.3972480170073
log likelihood for sigma_a=1.4860: -3152.5248760346626
log likelihood for sigma_a=1.4910: -3152.6522243836375
log likelihood for sigma_a=1.4960: -3152.779294698048
log likelihood for sigma_a=1.5010: -3152.9060885967237
log likelihood for sigma_a=1.5060: -3153.0326076835713
log likelihood for sigma_a=1.5110: -3153.158853547545
log likelihood for sigma_a=1.5160: -3153.284827763069
log likelihood for sigma_a=1.5210: -3153.410531889801
log likelihood for sigma_a=1.5260: -3153.535967473362
log likelihood for sigma_a=1.5310: -3153.661136045314
log likelihood for sigma_a=1.5360: -3153.7860391230047
log likelihood for sigma_a=1.5410: -3153.9106782102203
log likelihood for sigma_a=1.5460: -3154.035054796998
log likelihood for sigma_a=1.5510: -3154.159170360102
log likelihood for sigma_a=1.5560: -3154.283026363024
log likelihood for sigma_a=1.5610: -3154.4066242559807
log likelihood for sigma_a=1.5660: -3154.5299654763253
log likelihood for sigma_a=1.5710: -3154.653051448703
log likelihood for sigma_a=1.5760: -3154.7758835850304
log likelihood for sigma_a=1.5810: -3154.8984632847546
log likelihood for sigma_a=1.5860: -3155.020791935089
log likelihood for sigma_a=1.5910: -3155.142870910898
log likelihood for sigma_a=1.5960: -3155.2647015752514
log likelihood for sigma_a=1.6010: -3155.386285279129
log likelihood for sigma_a=1.6060: -3155.507623361739
log likelihood for sigma_a=1.6110: -3155.62871715088
log likelihood for sigma_a=1.6160: -3155.7495679626636
log likelihood for sigma_a=1.6210: -3155.8701771019623
log likelihood for sigma_a=1.6260: -3155.9905458623716
log likelihood for sigma_a=1.6310: -3156.1106755267147
log likelihood for sigma_a=1.6360: -3156.230567366321
log likelihood for sigma_a=1.6410: -3156.3502226422956
log likelihood for sigma_a=1.6460: -3156.4696426046153
log likelihood for sigma_a=1.6510: -3156.5888284927546
log likelihood for sigma_a=1.6560: -3156.707781536093
log likelihood for sigma_a=1.6610: -3156.82650295315
log likelihood for sigma_a=1.6660: -3156.9449939528104
log likelihood for sigma_a=1.6710: -3157.0632557333092
log likelihood for sigma_a=1.6760: -3157.1812894831974
log likelihood for sigma_a=1.6810: -3157.299096381309
log likelihood for sigma_a=1.6860: -3157.4166775964145
log likelihood for sigma_a=1.6910: -3157.534034287762
log likelihood for sigma_a=1.6960: -3157.6511676052396
log likelihood for sigma_a=1.7010: -3157.7680786889396
log likelihood for sigma_a=1.7060: -3157.8847686701006
log likelihood for sigma_a=1.7110: -3158.0012386703156
log likelihood for sigma_a=1.7160: -3158.1174898022564
log likelihood for sigma_a=1.7210: -3158.2335231696397
log likelihood for sigma_a=1.7260: -3158.3493398672053
log likelihood for sigma_a=1.7310: -3158.4649409807853
log likelihood for sigma_a=1.7360: -3158.5803275877347
log likelihood for sigma_a=1.7410: -3158.6955007566435
log likelihood for sigma_a=1.7460: -3158.810461547397
log likelihood for sigma_a=1.7510: -3158.925211011725
log likelihood for sigma_a=1.7560: -3159.039750192834
log likelihood for sigma_a=1.7610: -3159.154080125933
log likelihood for sigma_a=1.7660: -3159.268201837706
log likelihood for sigma_a=1.7710: -3159.382116346923
log likelihood for sigma_a=1.7760: -3159.4958246646042
log likelihood for sigma_a=1.7810: -3159.60932779345
log likelihood for sigma_a=1.7860: -3159.722626728524
log likelihood for sigma_a=1.7910: -3159.8357224572874
log likelihood for sigma_a=1.7960: -3159.948615959391
log likelihood for sigma_a=1.8010: -3160.0613082067684
log likelihood for sigma_a=1.8060: -3160.1738001643707
log likelihood for sigma_a=1.8110: -3160.286092789049
log likelihood for sigma_a=1.8160: -3160.3981870309835
log likelihood for sigma_a=1.8210: -3160.51008383253
log likelihood for sigma_a=1.8260: -3160.6217841292264
log likelihood for sigma_a=1.8310: -3160.7332888493625
log likelihood for sigma_a=1.8360: -3160.844598914171
log likelihood for sigma_a=1.8410: -3160.9557152380826
log likelihood for sigma_a=1.8460: -3161.0666387283495
log likelihood for sigma_a=1.8510: -3161.177370285702
log likelihood for sigma_a=1.8560: -3161.2879108039224
log likelihood for sigma_a=1.8610: -3161.398261170077
log likelihood for sigma_a=1.8660: -3161.5084222649884
log likelihood for sigma_a=1.8710: -3161.618394962393
log likelihood for sigma_a=1.8760: -3161.7281801299614
log likelihood for sigma_a=1.8810: -3161.8377786286715
log likelihood for sigma_a=1.8860: -3161.9471913133634
log likelihood for sigma_a=1.8910: -3162.0564190325504
log likelihood for sigma_a=1.8960: -3162.1654626283657
log likelihood for sigma_a=1.9010: -3162.2743229369116
log likelihood for sigma_a=1.9060: -3162.3830007883384
log likelihood for sigma_a=1.9110: -3162.4914970063023
log likelihood for sigma_a=1.9160: -3162.5998124089037
log likelihood for sigma_a=1.9210: -3162.7079478083147
log likelihood for sigma_a=1.9260: -3162.815904010475
log likelihood for sigma_a=1.9310: -3162.923681815959
log likelihood for sigma_a=1.9360: -3163.0312820191557
log likelihood for sigma_a=1.9410: -3163.1387054092856
log likelihood for sigma_a=1.9460: -3163.245952769396
log likelihood for sigma_a=1.9510: -3163.3530248774105
log likelihood for sigma_a=1.9560: -3163.4599225052866
log likelihood for sigma_a=1.9610: -3163.5666464199667
log likelihood for sigma_a=1.9660: -3163.6731973826677
log likelihood for sigma_a=1.9710: -3163.779576149239
log likelihood for sigma_a=1.9760: -3163.88578347032
log likelihood for sigma_a=1.9810: -3163.991820091281
log likelihood for sigma_a=1.9860: -3164.097686752207
log likelihood for sigma_a=1.9910: -3164.2033841881002
log likelihood for sigma_a=1.9960: -3164.308913128729
max log likelihood: -3092.8602669458705, max sigma_a: 0.096
Gradient acent
Code for ssm.learning.torch_lbfgs_optimize_SS_tracking_diagV0
m0_torch = torch.from_numpy(m0)
sqrt_diag_V0_torch = torch.DoubleTensor([sqrt_diag_V0_value
for i in range(len(m0))])
y_torch = torch.from_numpy(y.astype(np.double))
B_torch = torch.from_numpy(B.astype(np.double))
Qe_torch = torch.from_numpy(Qe.astype(np.double))
Z_torch = torch.from_numpy(Z.astype(np.double))
optim_res_ga = ssm.learning.torch_lbfgs_optimize_SS_tracking_diagV0(
y=y_torch, B=B_torch, sigma_a0=sigma_a0,
Qe=Qe_torch, Z=Z_torch, pos_x_R_std0=sigma_x, pos_y_R_std0=sigma_y, m0_0=m0_torch,
sqrt_diag_V0_0=sqrt_diag_V0_torch, max_iter=lbfgs_max_iter,
n_epochs=lbfgs_n_epochs,
vars_to_estimate=ga_vars_to_estimate, tolerance_grad=lbfgs_tolerance_grad,
tolerance_change=lbfgs_tolerance_change)
/nfs/ghome/live/rapela/dev/work/ucl/gatsby-swc/gatsby/code/ssm_rapela/repos/ssm/src/ssm/inference.py:395: UserWarning:
The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3571.)
--------------------------------------------------------------------------------
startup
likelihood: -3102.3046470886297
ll=-3102.3046470886297, sigma_a=0.25
ll=-3129.3688077777233, sigma_a=-0.75
ll=-3182.1228477491404, sigma_a=0.01739221520360048
ll=-3092.8879637639525, sigma_a=0.10134069188145016
--------------------------------------------------------------------------------
epoch: 0
likelihood: -3092.8879637639525
sigma_a:
tensor([0.1013], dtype=torch.float64, requires_grad=True)
ll=-3092.8879637639525, sigma_a=0.10134069188145016
ll=-3094.1492421031144, sigma_a=0.07190557315072493
ll=-3092.8602089744095, sigma_a=0.09602068534331629
--------------------------------------------------------------------------------
epoch: 1
likelihood: -3092.8602089744095
sigma_a:
tensor([0.0960], dtype=torch.float64, requires_grad=True)
ll=-3092.8602089744095, sigma_a=0.09602068534331629
ll=-3092.859512234081, sigma_a=0.09678874009803548
--------------------------------------------------------------------------------
epoch: 2
likelihood: -3092.859512234081
sigma_a:
tensor([0.0968], dtype=torch.float64, requires_grad=True)
ll=-3092.859512234081, sigma_a=0.09678874009803548
ll=-3092.859505816345, sigma_a=0.0967223956397874
--------------------------------------------------------------------------------
epoch: 3
likelihood: -3092.859505816345
sigma_a:
tensor([0.0967], dtype=torch.float64, requires_grad=True)
ll=-3092.859505816345, sigma_a=0.0967223956397874
--------------------------------------------------------------------------------
epoch: 4
likelihood: -3092.859505816345
sigma_a:
tensor([0.0967], dtype=torch.float64, requires_grad=True)
EM
Code for ssm.learning.em_SS_tracking
Qe_reg_param = 1e-5
Qe_regularized = Qe + Qe_reg_param*np.eye(Qe.shape[0])
optim_res_em = ssm.learning.em_SS_tracking(
y=y, B=B, sigma_a0=sigma_a0,
Qe=Qe_regularized, Z=Z, R_0=R, m0_0=m0, V0_0=V0,
vars_to_estimate=em_vars_to_estimate, max_iter=em_max_iter)
LogLike[0000]=-3102.304785
sigma_a=0.250000
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0001]=-3102.250899
sigma_a=0.249255
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0002]=-3102.197345
sigma_a=0.248515
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0003]=-3102.144203
sigma_a=0.247781
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0004]=-3102.091485
sigma_a=0.247054
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0005]=-3102.038657
sigma_a=0.246325
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0006]=-3101.984614
sigma_a=0.245580
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0007]=-3101.930526
sigma_a=0.244835
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0008]=-3101.879891
sigma_a=0.244138
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0009]=-3101.827735
sigma_a=0.243420
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0010]=-3101.777476
sigma_a=0.242729
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0011]=-3101.727865
sigma_a=0.242046
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0012]=-3101.674626
sigma_a=0.241315
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0013]=-3101.622873
sigma_a=0.240604
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0014]=-3101.573323
sigma_a=0.239924
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0015]=-3101.524218
sigma_a=0.239250
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0016]=-3101.473775
sigma_a=0.238558
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0017]=-3101.424115
sigma_a=0.237877
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0018]=-3101.371854
sigma_a=0.237161
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0019]=-3101.321096
sigma_a=0.236466
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0020]=-3101.269676
sigma_a=0.235762
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0021]=-3101.222807
sigma_a=0.235120
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0022]=-3101.175026
sigma_a=0.234467
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0023]=-3101.125542
sigma_a=0.233790
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0024]=-3101.078329
sigma_a=0.233144
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0025]=-3101.030483
sigma_a=0.232490
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0026]=-3100.980132
sigma_a=0.231803
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0027]=-3100.934198
sigma_a=0.231175
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0028]=-3100.885372
sigma_a=0.230509
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0029]=-3100.836632
sigma_a=0.229844
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0030]=-3100.792898
sigma_a=0.229247
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0031]=-3100.744389
sigma_a=0.228585
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0032]=-3100.699162
sigma_a=0.227969
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0033]=-3100.652907
sigma_a=0.227338
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0034]=-3100.603175
sigma_a=0.226660
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0035]=-3100.556881
sigma_a=0.226030
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0036]=-3100.509363
sigma_a=0.225382
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0037]=-3100.463853
sigma_a=0.224763
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0038]=-3100.418837
sigma_a=0.224150
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0039]=-3100.372309
sigma_a=0.223516
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0040]=-3100.326390
sigma_a=0.222892
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0041]=-3100.283028
sigma_a=0.222302
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0042]=-3100.236297
sigma_a=0.221666
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0043]=-3100.192484
sigma_a=0.221070
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0044]=-3100.151508
sigma_a=0.220513
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0045]=-3100.107966
sigma_a=0.219921
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0046]=-3100.063660
sigma_a=0.219318
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0047]=-3100.020333
sigma_a=0.218729
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0048]=-3099.976467
sigma_a=0.218133
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0049]=-3099.933164
sigma_a=0.217545
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0050]=-3099.890977
sigma_a=0.216971
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0051]=-3099.849481
sigma_a=0.216408
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0052]=-3099.807084
sigma_a=0.215832
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0053]=-3099.766036
sigma_a=0.215274
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0054]=-3099.725405
sigma_a=0.214722
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0055]=-3099.684846
sigma_a=0.214171
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0056]=-3099.641684
sigma_a=0.213585
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0057]=-3099.601002
sigma_a=0.213032
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0058]=-3099.561760
sigma_a=0.212499
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0059]=-3099.523105
sigma_a=0.211974
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0060]=-3099.485444
sigma_a=0.211463
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0061]=-3099.440968
sigma_a=0.210859
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0062]=-3099.401986
sigma_a=0.210329
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0063]=-3099.360288
sigma_a=0.209763
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0064]=-3099.320907
sigma_a=0.209228
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0065]=-3099.280397
sigma_a=0.208678
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0066]=-3099.241102
sigma_a=0.208144
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0067]=-3099.204803
sigma_a=0.207651
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0068]=-3099.161157
sigma_a=0.207058
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0069]=-3099.121327
sigma_a=0.206516
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0070]=-3099.082099
sigma_a=0.205983
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0071]=-3099.039923
sigma_a=0.205410
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0072]=-3099.002050
sigma_a=0.204895
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0073]=-3098.964479
sigma_a=0.204385
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0074]=-3098.925256
sigma_a=0.203851
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0075]=-3098.887665
sigma_a=0.203340
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0076]=-3098.850544
sigma_a=0.202835
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0077]=-3098.811449
sigma_a=0.202303
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0078]=-3098.771482
sigma_a=0.201760
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0079]=-3098.734506
sigma_a=0.201256
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0080]=-3098.700231
sigma_a=0.200790
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0081]=-3098.668386
sigma_a=0.200356
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0082]=-3098.631926
sigma_a=0.199860
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0083]=-3098.592084
sigma_a=0.199317
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0084]=-3098.555566
sigma_a=0.198820
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0085]=-3098.520816
sigma_a=0.198346
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0086]=-3098.485147
sigma_a=0.197860
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0087]=-3098.451129
sigma_a=0.197396
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0088]=-3098.415875
sigma_a=0.196916
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0089]=-3098.381156
sigma_a=0.196442
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0090]=-3098.345827
sigma_a=0.195960
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0091]=-3098.311312
sigma_a=0.195489
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0092]=-3098.274390
sigma_a=0.194985
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0093]=-3098.239846
sigma_a=0.194513
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0094]=-3098.206226
sigma_a=0.194053
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0095]=-3098.170258
sigma_a=0.193562
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0096]=-3098.140839
sigma_a=0.193159
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0097]=-3098.109422
sigma_a=0.192730
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0098]=-3098.075102
sigma_a=0.192260
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0099]=-3098.043104
sigma_a=0.191822
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0100]=-3098.009978
sigma_a=0.191368
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0101]=-3097.977876
sigma_a=0.190929
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0102]=-3097.945526
sigma_a=0.190485
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0103]=-3097.914155
sigma_a=0.190055
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0104]=-3097.879497
sigma_a=0.189580
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0105]=-3097.847685
sigma_a=0.189143
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0106]=-3097.815247
sigma_a=0.188698
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0107]=-3097.781652
sigma_a=0.188236
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0108]=-3097.748648
sigma_a=0.187783
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0109]=-3097.715855
sigma_a=0.187332
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0110]=-3097.685567
sigma_a=0.186915
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0111]=-3097.652558
sigma_a=0.186461
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0112]=-3097.621492
sigma_a=0.186033
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0113]=-3097.592520
sigma_a=0.185634
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0114]=-3097.558575
sigma_a=0.185166
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0115]=-3097.524596
sigma_a=0.184697
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0116]=-3097.494817
sigma_a=0.184286
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0117]=-3097.465452
sigma_a=0.183881
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0118]=-3097.435000
sigma_a=0.183460
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0119]=-3097.404391
sigma_a=0.183037
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0120]=-3097.375267
sigma_a=0.182634
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0121]=-3097.345358
sigma_a=0.182220
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0122]=-3097.317400
sigma_a=0.181832
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0123]=-3097.292957
sigma_a=0.181494
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0124]=-3097.266839
sigma_a=0.181131
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0125]=-3097.240916
sigma_a=0.180772
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0126]=-3097.211728
sigma_a=0.180366
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0127]=-3097.184998
sigma_a=0.179995
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0128]=-3097.156186
sigma_a=0.179594
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0129]=-3097.128053
sigma_a=0.179203
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0130]=-3097.098334
sigma_a=0.178789
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0131]=-3097.069607
sigma_a=0.178389
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0132]=-3097.042805
sigma_a=0.178015
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0133]=-3097.018091
sigma_a=0.177671
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0134]=-3096.988303
sigma_a=0.177255
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0135]=-3096.961532
sigma_a=0.176881
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0136]=-3096.935357
sigma_a=0.176515
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0137]=-3096.908146
sigma_a=0.176134
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0138]=-3096.878570
sigma_a=0.175720
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0139]=-3096.847825
sigma_a=0.175289
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0140]=-3096.822723
sigma_a=0.174937
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0141]=-3096.795432
sigma_a=0.174554
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0142]=-3096.766505
sigma_a=0.174147
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0143]=-3096.738930
sigma_a=0.173759
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0144]=-3096.713027
sigma_a=0.173395
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0145]=-3096.690294
sigma_a=0.173075
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0146]=-3096.661504
sigma_a=0.172669
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0147]=-3096.634741
sigma_a=0.172291
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0148]=-3096.605608
sigma_a=0.171879
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0149]=-3096.580618
sigma_a=0.171526
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0150]=-3096.554015
sigma_a=0.171150
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0151]=-3096.527274
sigma_a=0.170771
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0152]=-3096.501944
sigma_a=0.170412
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0153]=-3096.477120
sigma_a=0.170059
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0154]=-3096.451533
sigma_a=0.169696
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0155]=-3096.427785
sigma_a=0.169358
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0156]=-3096.404395
sigma_a=0.169025
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0157]=-3096.380147
sigma_a=0.168680
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0158]=-3096.356252
sigma_a=0.168339
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0159]=-3096.334491
sigma_a=0.168029
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0160]=-3096.313201
sigma_a=0.167724
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0161]=-3096.288501
sigma_a=0.167371
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0162]=-3096.266850
sigma_a=0.167061
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0163]=-3096.240738
sigma_a=0.166687
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0164]=-3096.219208
sigma_a=0.166378
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0165]=-3096.199877
sigma_a=0.166101
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0166]=-3096.175848
sigma_a=0.165755
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0167]=-3096.154581
sigma_a=0.165449
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0168]=-3096.132144
sigma_a=0.165126
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0169]=-3096.112014
sigma_a=0.164836
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0170]=-3096.093471
sigma_a=0.164569
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0171]=-3096.070477
sigma_a=0.164236
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0172]=-3096.050027
sigma_a=0.163941
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0173]=-3096.029757
sigma_a=0.163647
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0174]=-3096.009671
sigma_a=0.163356
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0175]=-3095.987322
sigma_a=0.163032
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0176]=-3095.964621
sigma_a=0.162702
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0177]=-3095.944207
sigma_a=0.162406
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0178]=-3095.923180
sigma_a=0.162100
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0179]=-3095.900760
sigma_a=0.161773
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0180]=-3095.880659
sigma_a=0.161480
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0181]=-3095.860786
sigma_a=0.161189
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0182]=-3095.840759
sigma_a=0.160897
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0183]=-3095.822034
sigma_a=0.160622
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0184]=-3095.802001
sigma_a=0.160329
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0185]=-3095.785991
sigma_a=0.160094
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0186]=-3095.764496
sigma_a=0.159778
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0187]=-3095.744624
sigma_a=0.159486
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0188]=-3095.727312
sigma_a=0.159231
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0189]=-3095.707226
sigma_a=0.158935
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0190]=-3095.688002
sigma_a=0.158651
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0191]=-3095.671248
sigma_a=0.158404
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0192]=-3095.652711
sigma_a=0.158130
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0193]=-3095.632097
sigma_a=0.157824
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0194]=-3095.615594
sigma_a=0.157580
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0195]=-3095.594495
sigma_a=0.157266
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0196]=-3095.577711
sigma_a=0.157017
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0197]=-3095.560715
sigma_a=0.156764
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0198]=-3095.539135
sigma_a=0.156442
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
LogLike[0199]=-3095.520744
sigma_a=0.156168
R:
[[25. 0.]
[ 0. 25.]]
m0:
[0. 0. 0. 0. 0. 0.]
V0:
[[1.e-06 0.e+00 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 1.e-06 0.e+00 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 1.e-06 0.e+00 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 1.e-06 0.e+00 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 1.e-06 0.e+00]
[0.e+00 0.e+00 0.e+00 0.e+00 0.e+00 1.e-06]]
Plots
Convergence
fig = go.Figure()
trace = go.Scatter(x=optim_res_ga["elapsed_time"], y=optim_res_ga["log_like"],
name="Gradient ascent", mode="lines+markers")
fig.add_trace(trace)
trace = go.Scatter(x=optim_res_em["elapsed_time"], y=optim_res_em["log_like"],
name="EM", mode="lines+markers")
fig.add_trace(trace)
fig.update_layout(xaxis_title="Elapsed Time (sec)",
yaxis_title="Log Likelihood")
fig
\(\sigma_a\) estimates
fig = go.Figure()
trace = go.Bar(x=["Grid Search", "Gradient Ascent", "EM"],
y=[gs_max_sigma_a**2,
optim_res_ga["estimates"]["sigma_a"].item()**2,
optim_res_em["estimates"]["sigma_a"]**2])
fig.add_trace(trace)
fig.add_hline(y=sigma_a_true**2)
fig.update_layout(xaxis_title="Estimation Method",
yaxis_title=r"$\sigma_a$")
fig
Estimation of all parameters
pos_x0_0 = 0.1
pos_y0_0 = -0.1
vel_x0_0 = 0.01
vel_y0_0 = -0.01
acc_x0_0 = 0.001
acc_y0_0 = -0.001
sqrt_diag_V0_value0 = 1e-2
sigma_a0 = 0.25
sigma_x_0 = 5e-2
sigma_y_0 = 5e-2
ga_vars_to_estimate = {"sigma_a": True,
"pos_x_R_std": True, "pos_y_R_std": True, "m0": True,
"sqrt_diag_V0": True}
em_vars_to_estimate = {"sigma_a": True, "R": True, "m0": True,
"V0": True}
em_max_iter = 400
lbfgs_max_iter = 2
lbfgs_n_epochs = 200
lbfgs_tolerance_grad = 1e-9
lbfgs_tolerance_change = 1e-7
m0_0 = np.array([pos_x0_0, vel_x0_0, acc_x0_0, pos_y0_0, vel_y0_0, acc_y0_0])
sqrt_diag_V0_0 = np.array([sqrt_diag_V0_value0 for i in range(len(m0_0))])
sqrt_diag_R_0 = np.array([sigma_x_0, sigma_y_0])
Gradient acent
Code for ssm.learning.torch_lbfgs_optimize_SS_tracking_diagV0
m0_0_torch = torch.from_numpy(m0_0)
sqrt_diag_V0_0_torch = torch.from_numpy(sqrt_diag_V0_0)
sqrt_diag_R_0_torch = torch.from_numpy(sqrt_diag_R_0)
y_torch = torch.from_numpy(y.astype(np.double))
B_torch = torch.from_numpy(B.astype(np.double))
Qe_torch = torch.from_numpy(Qe.astype(np.double))
Z_torch = torch.from_numpy(Z.astype(np.double))
optim_res_ga = ssm.learning.torch_lbfgs_optimize_SS_tracking_diagV0(
y=y_torch, B=B_torch, sigma_a0=sigma_a0,
Qe=Qe_torch, Z=Z_torch, pos_x_R_std0=sqrt_diag_R_0[0],
pos_y_R_std0=sqrt_diag_R_0[1], m0_0=m0_0_torch,
sqrt_diag_V0_0=sqrt_diag_V0_0_torch, max_iter=lbfgs_max_iter,
n_epochs=lbfgs_n_epochs,
vars_to_estimate=ga_vars_to_estimate, tolerance_grad=lbfgs_tolerance_grad,
tolerance_change=lbfgs_tolerance_change)
--------------------------------------------------------------------------------
startup
likelihood: -4455759.282957736
ll=-4455759.282957736, sigma_a=0.25, pos_x_R_std=0.05, pos_y_R_std=0.05, m0=tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0100, 0.0100, 0.0100, 0.0100, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-40787.50619013615, sigma_a=0.2545681472031047, pos_x_R_std=0.5276505377270831, pos_y_R_std=0.5637062325609771, m0=tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 0
likelihood: -40787.50619013615
sigma_a:
tensor([0.2546], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([0.5277], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([0.5637], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-40787.50619013615, sigma_a=0.2545681472031047, pos_x_R_std=0.5276505377270831, pos_y_R_std=0.5637062325609771, m0=tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-40727.61342143312, sigma_a=0.2545883339353004, pos_x_R_std=0.528092406519629, pos_y_R_std=0.5640883521633027, m0=tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-40459.500003952635, sigma_a=0.2546792751638418, pos_x_R_std=0.5300830254300478, pos_y_R_std=0.5658098009717792, m0=tensor([ 0.1000, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-39032.22083422173, sigma_a=0.2551798056933014, pos_x_R_std=0.5410391731879408, pos_y_R_std=0.5752844660644315, m0=tensor([ 0.1001, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-32426.46482287707, sigma_a=0.25793477155183364, pos_x_R_std=0.6013428135005898, pos_y_R_std=0.6274338901554396, m0=tensor([ 0.1001, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0136, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 1
likelihood: -32426.46482287707
sigma_a:
tensor([0.2579], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([0.6013], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([0.6274], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1001, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0136, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-32426.46482287707, sigma_a=0.25793477155183364, pos_x_R_std=0.6013428135005898, pos_y_R_std=0.6274338901554396, m0=tensor([ 0.1001, 0.0100, 0.0010, -0.1000, -0.0100, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0136, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-20924.31819524828, sigma_a=0.26766971734408074, pos_x_R_std=0.7555793907928443, pos_y_R_std=0.7940344164100972, m0=tensor([ 0.1004, 0.0101, 0.0010, -0.1000, -0.0099, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 2
likelihood: -20924.31819524828
sigma_a:
tensor([0.2677], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([0.7556], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([0.7940], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1004, 0.0101, 0.0010, -0.1000, -0.0099, -0.0010],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-20924.31819524828, sigma_a=0.26766971734408074, pos_x_R_std=0.7555793907928443, pos_y_R_std=0.7940344164100972, m0=tensor([ 0.1004, 0.0101, 0.0010, -0.1000, -0.0099, -0.0010],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-14900.502024036528, sigma_a=0.27924206914603933, pos_x_R_std=0.9113340700474011, pos_y_R_std=0.9547642338078087, m0=tensor([ 0.1008, 0.0101, 0.0010, -0.1000, -0.0098, -0.0009],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 3
likelihood: -14900.502024036528
sigma_a:
tensor([0.2792], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([0.9113], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([0.9548], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1008, 0.0101, 0.0010, -0.1000, -0.0098, -0.0009],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-14900.502024036528, sigma_a=0.27924206914603933, pos_x_R_std=0.9113340700474011, pos_y_R_std=0.9547642338078087, m0=tensor([ 0.1008, 0.0101, 0.0010, -0.1000, -0.0098, -0.0009],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0138, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-10507.959364648561, sigma_a=0.2961095971278169, pos_x_R_std=1.1146865469737233, pos_y_R_std=1.168118543875824, m0=tensor([ 0.1015, 0.0103, 0.0010, -0.0999, -0.0096, -0.0009],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0137, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 4
likelihood: -10507.959364648561
sigma_a:
tensor([0.2961], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([1.1147], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([1.1681], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1015, 0.0103, 0.0010, -0.0999, -0.0096, -0.0009],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0137, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-10507.959364648561, sigma_a=0.2961095971278169, pos_x_R_std=1.1146865469737233, pos_y_R_std=1.168118543875824, m0=tensor([ 0.1015, 0.0103, 0.0010, -0.0999, -0.0096, -0.0009],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0137, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-7724.984707118879, sigma_a=0.31630304500116435, pos_x_R_std=1.35265485582925, pos_y_R_std=1.4161979797041437, m0=tensor([ 0.1026, 0.0104, 0.0009, -0.0997, -0.0092, -0.0008],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 5
likelihood: -7724.984707118879
sigma_a:
tensor([0.3163], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([1.3527], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([1.4162], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1026, 0.0104, 0.0009, -0.0997, -0.0092, -0.0008],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-7724.984707118879, sigma_a=0.31630304500116435, pos_x_R_std=1.35265485582925, pos_y_R_std=1.4161979797041437, m0=tensor([ 0.1026, 0.0104, 0.0009, -0.0997, -0.0092, -0.0008],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0135, 0.0102, 0.0100, 0.0103, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-5890.5290528785345, sigma_a=0.33858154378563265, pos_x_R_std=1.6406597515072485, pos_y_R_std=1.7166107636553385, m0=tensor([ 0.1043, 0.0107, 0.0009, -0.0995, -0.0086, -0.0006],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0132, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 6
likelihood: -5890.5290528785345
sigma_a:
tensor([0.3386], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([1.6407], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([1.7166], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1043, 0.0107, 0.0009, -0.0995, -0.0086, -0.0006],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0132, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-5890.5290528785345, sigma_a=0.33858154378563265, pos_x_R_std=1.6406597515072485, pos_y_R_std=1.7166107636553385, m0=tensor([ 0.1043, 0.0107, 0.0009, -0.0995, -0.0086, -0.0006],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0132, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-4724.263970287377, sigma_a=0.3571299274956437, pos_x_R_std=1.9803132598438002, pos_y_R_std=2.0699805178912483, m0=tensor([ 0.1070, 0.0111, 0.0008, -0.0990, -0.0077, -0.0004],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0128, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 7
likelihood: -4724.263970287377
sigma_a:
tensor([0.3571], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([1.9803], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([2.0700], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1070, 0.0111, 0.0008, -0.0990, -0.0077, -0.0004],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0128, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-4724.263970287377, sigma_a=0.3571299274956437, pos_x_R_std=1.9803132598438002, pos_y_R_std=2.0699805178912483, m0=tensor([ 0.1070, 0.0111, 0.0008, -0.0990, -0.0077, -0.0004],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0128, 0.0101, 0.0100, 0.0103, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-3994.818036575852, sigma_a=0.3597027484519859, pos_x_R_std=2.377492077415295, pos_y_R_std=2.4819792719280285, m0=tensor([ 1.1107e-01, 1.1806e-02, 5.9668e-04, -9.8241e-02, -6.1924e-03,
-3.8247e-06], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0121, 0.0100, 0.0100, 0.0102, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 8
likelihood: -3994.818036575852
sigma_a:
tensor([0.3597], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([2.3775], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([2.4820], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.1107e-01, 1.1806e-02, 5.9668e-04, -9.8241e-02, -6.1924e-03,
-3.8247e-06], dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0121, 0.0100, 0.0100, 0.0102, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-3994.818036575852, sigma_a=0.3597027484519859, pos_x_R_std=2.377492077415295, pos_y_R_std=2.4819792719280285, m0=tensor([ 1.1107e-01, 1.1806e-02, 5.9668e-04, -9.8241e-02, -6.1924e-03,
-3.8247e-06], dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0121, 0.0100, 0.0100, 0.0102, 0.0101, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-3554.3960820131865, sigma_a=0.3188431125383502, pos_x_R_std=2.8312963385008234, pos_y_R_std=2.9502090692278182, m0=tensor([ 0.1174, 0.0129, 0.0003, -0.0970, -0.0039, 0.0004],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0114, 0.0100, 0.0100, 0.0102, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 9
likelihood: -3554.3960820131865
sigma_a:
tensor([0.3188], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([2.8313], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([2.9502], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1174, 0.0129, 0.0003, -0.0970, -0.0039, 0.0004],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0114, 0.0100, 0.0100, 0.0102, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-3554.3960820131865, sigma_a=0.3188431125383502, pos_x_R_std=2.8312963385008234, pos_y_R_std=2.9502090692278182, m0=tensor([ 0.1174, 0.0129, 0.0003, -0.0970, -0.0039, 0.0004],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0114, 0.0100, 0.0100, 0.0102, 0.0100, 0.0100], dtype=torch.float64,
requires_grad=True)
ll=-3295.134875828525, sigma_a=0.1688547923676056, pos_x_R_std=3.3383123513614548, pos_y_R_std=3.468569425178536, m0=tensor([ 0.1270, 0.0146, -0.0006, -0.0952, -0.0005, 0.0007],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0104, 0.0098, 0.0099, 0.0101, 0.0100, 0.0099], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 10
likelihood: -3295.134875828525
sigma_a:
tensor([0.1689], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([3.3383], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([3.4686], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1270, 0.0146, -0.0006, -0.0952, -0.0005, 0.0007],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0104, 0.0098, 0.0099, 0.0101, 0.0100, 0.0099], dtype=torch.float64,
requires_grad=True)
ll=-3295.134875828525, sigma_a=0.1688547923676056, pos_x_R_std=3.3383123513614548, pos_y_R_std=3.468569425178536, m0=tensor([ 0.1270, 0.0146, -0.0006, -0.0952, -0.0005, 0.0007],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0104, 0.0098, 0.0099, 0.0101, 0.0100, 0.0099], dtype=torch.float64,
requires_grad=True)
ll=-3164.2634812284614, sigma_a=-0.15916047173742964, pos_x_R_std=3.882091217239262, pos_y_R_std=4.019258836657299, m0=tensor([ 0.1410, 0.0134, -0.0088, -0.0924, 0.0034, -0.0042],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0093, 0.0095, 0.0097, 0.0100, 0.0097, 0.0096], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 11
likelihood: -3164.2634812284614
sigma_a:
tensor([-0.1592], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([3.8821], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([4.0193], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1410, 0.0134, -0.0088, -0.0924, 0.0034, -0.0042],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0093, 0.0095, 0.0097, 0.0100, 0.0097, 0.0096], dtype=torch.float64,
requires_grad=True)
ll=-3164.2634812284614, sigma_a=-0.15916047173742964, pos_x_R_std=3.882091217239262, pos_y_R_std=4.019258836657299, m0=tensor([ 0.1410, 0.0134, -0.0088, -0.0924, 0.0034, -0.0042],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0093, 0.0095, 0.0097, 0.0100, 0.0097, 0.0096], dtype=torch.float64,
requires_grad=True)
ll=-3114.1936479008555, sigma_a=0.06353883245386724, pos_x_R_std=4.361468611028386, pos_y_R_std=4.504164048859709, m0=tensor([ 0.1586, 0.0095, -0.0237, -0.0887, 0.0077, -0.0140],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0083, 0.0090, 0.0094, 0.0099, 0.0092, 0.0092], dtype=torch.float64,
requires_grad=True)
ll=-3200.584473248271, sigma_a=1.0667991978356597, pos_x_R_std=6.5210637700483876, pos_y_R_std=6.688662029831563, m0=tensor([ 0.2379, -0.0082, -0.0909, -0.0725, 0.0269, -0.0581],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0038, 0.0068, 0.0079, 0.0091, 0.0072, 0.0072], dtype=torch.float64,
requires_grad=True)
ll=-3108.226174527317, sigma_a=0.3405453236472269, pos_x_R_std=4.95774640494945, pos_y_R_std=5.107317668406627, m0=tensor([ 0.1805, 0.0046, -0.0423, -0.0843, 0.0130, -0.0262],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0084, 0.0090, 0.0097, 0.0087, 0.0086], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 12
likelihood: -3108.226174527317
sigma_a:
tensor([0.3405], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9577], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1073], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1805, 0.0046, -0.0423, -0.0843, 0.0130, -0.0262],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0071, 0.0084, 0.0090, 0.0097, 0.0087, 0.0086], dtype=torch.float64,
requires_grad=True)
ll=-3108.226174527317, sigma_a=0.3405453236472269, pos_x_R_std=4.95774640494945, pos_y_R_std=5.107317668406627, m0=tensor([ 0.1805, 0.0046, -0.0423, -0.0843, 0.0130, -0.0262],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0084, 0.0090, 0.0097, 0.0087, 0.0086], dtype=torch.float64,
requires_grad=True)
ll=-3092.2774167736766, sigma_a=0.0922006816993648, pos_x_R_std=4.952336516945139, pos_y_R_std=5.086230270855736, m0=tensor([ 0.1885, 0.0047, -0.0455, -0.0828, 0.0153, -0.0281],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0083, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 13
likelihood: -3092.2774167736766
sigma_a:
tensor([0.0922], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9523], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.0862], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1885, 0.0047, -0.0455, -0.0828, 0.0153, -0.0281],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0071, 0.0083, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.2774167736766, sigma_a=0.0922006816993648, pos_x_R_std=4.952336516945139, pos_y_R_std=5.086230270855736, m0=tensor([ 0.1885, 0.0047, -0.0455, -0.0828, 0.0153, -0.0281],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0083, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.785046378573, sigma_a=0.1185457806145715, pos_x_R_std=4.970608867646607, pos_y_R_std=5.104575652323012, m0=tensor([ 0.1904, -0.0016, -0.0575, -0.0826, 0.0129, -0.0392],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0070, 0.0082, 0.0089, 0.0095, 0.0084, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.246711333806, sigma_a=0.09683179744490589, pos_x_R_std=4.955548551612915, pos_y_R_std=5.089455143354583, m0=tensor([ 0.1888, 0.0036, -0.0476, -0.0828, 0.0149, -0.0300],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0082, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 14
likelihood: -3092.246711333806
sigma_a:
tensor([0.0968], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9555], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.0895], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1888, 0.0036, -0.0476, -0.0828, 0.0149, -0.0300],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0071, 0.0082, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.246711333806, sigma_a=0.09683179744490589, pos_x_R_std=4.955548551612915, pos_y_R_std=5.089455143354583, m0=tensor([ 0.1888, 0.0036, -0.0476, -0.0828, 0.0149, -0.0300],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0071, 0.0082, 0.0089, 0.0096, 0.0085, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.124251597467, sigma_a=0.0953273455850997, pos_x_R_std=4.983814664990147, pos_y_R_std=5.116475160262085, m0=tensor([ 0.1916, -0.0013, -0.0575, -0.0824, 0.0135, -0.0389],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0070, 0.0082, 0.0089, 0.0095, 0.0084, 0.0085], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 15
likelihood: -3092.124251597467
sigma_a:
tensor([0.0953], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9838], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1165], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.1916, -0.0013, -0.0575, -0.0824, 0.0135, -0.0389],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0070, 0.0082, 0.0089, 0.0095, 0.0084, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3092.124251597467, sigma_a=0.0953273455850997, pos_x_R_std=4.983814664990147, pos_y_R_std=5.116475160262085, m0=tensor([ 0.1916, -0.0013, -0.0575, -0.0824, 0.0135, -0.0389],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0070, 0.0082, 0.0089, 0.0095, 0.0084, 0.0085], dtype=torch.float64,
requires_grad=True)
ll=-3091.7182998581434, sigma_a=0.09040897000649308, pos_x_R_std=5.023851964146825, pos_y_R_std=5.151079367134098, m0=tensor([ 0.2039, -0.0317, -0.1174, -0.0808, 0.0032, -0.0947],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0068, 0.0078, 0.0090, 0.0093, 0.0076, 0.0084], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 16
likelihood: -3091.7182998581434
sigma_a:
tensor([0.0904], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0239], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1511], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.2039, -0.0317, -0.1174, -0.0808, 0.0032, -0.0947],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0068, 0.0078, 0.0090, 0.0093, 0.0076, 0.0084], dtype=torch.float64,
requires_grad=True)
ll=-3091.7182998581434, sigma_a=0.09040897000649308, pos_x_R_std=5.023851964146825, pos_y_R_std=5.151079367134098, m0=tensor([ 0.2039, -0.0317, -0.1174, -0.0808, 0.0032, -0.0947],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0068, 0.0078, 0.0090, 0.0093, 0.0076, 0.0084], dtype=torch.float64,
requires_grad=True)
ll=-3091.1613609211217, sigma_a=0.0804024183513666, pos_x_R_std=5.059368842433065, pos_y_R_std=5.176647452541971, m0=tensor([ 0.2399, -0.1109, -0.2757, -0.0741, -0.0200, -0.2464],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0064, 0.0081, 0.0087, 0.0053, 0.0072], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 17
likelihood: -3091.1613609211217
sigma_a:
tensor([0.0804], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0594], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1766], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.2399, -0.1109, -0.2757, -0.0741, -0.0200, -0.2464],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0065, 0.0064, 0.0081, 0.0087, 0.0053, 0.0072], dtype=torch.float64,
requires_grad=True)
ll=-3091.1613609211217, sigma_a=0.0804024183513666, pos_x_R_std=5.059368842433065, pos_y_R_std=5.176647452541971, m0=tensor([ 0.2399, -0.1109, -0.2757, -0.0741, -0.0200, -0.2464],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0064, 0.0081, 0.0087, 0.0053, 0.0072], dtype=torch.float64,
requires_grad=True)
ll=-3091.0075551260643, sigma_a=0.08312295927095419, pos_x_R_std=5.0349163038578375, pos_y_R_std=5.152310814615106, m0=tensor([ 0.2507, -0.1243, -0.3042, -0.0707, -0.0202, -0.2768],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0059, 0.0073, 0.0086, 0.0048, 0.0065], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 18
likelihood: -3091.0075551260643
sigma_a:
tensor([0.0831], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0349], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1523], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.2507, -0.1243, -0.3042, -0.0707, -0.0202, -0.2768],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0065, 0.0059, 0.0073, 0.0086, 0.0048, 0.0065], dtype=torch.float64,
requires_grad=True)
ll=-3091.0075551260643, sigma_a=0.08312295927095419, pos_x_R_std=5.0349163038578375, pos_y_R_std=5.152310814615106, m0=tensor([ 0.2507, -0.1243, -0.3042, -0.0707, -0.0202, -0.2768],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0059, 0.0073, 0.0086, 0.0048, 0.0065], dtype=torch.float64,
requires_grad=True)
ll=-3090.9403788895393, sigma_a=0.08399569293660554, pos_x_R_std=4.997922187925673, pos_y_R_std=5.116555088219359, m0=tensor([ 0.2615, -0.1333, -0.3248, -0.0667, -0.0177, -0.3007],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0055, 0.0064, 0.0085, 0.0043, 0.0057], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 19
likelihood: -3090.9403788895393
sigma_a:
tensor([0.0840], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9979], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1166], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.2615, -0.1333, -0.3248, -0.0667, -0.0177, -0.3007],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0065, 0.0055, 0.0064, 0.0085, 0.0043, 0.0057], dtype=torch.float64,
requires_grad=True)
ll=-3090.9403788895393, sigma_a=0.08399569293660554, pos_x_R_std=4.997922187925673, pos_y_R_std=5.116555088219359, m0=tensor([ 0.2615, -0.1333, -0.3248, -0.0667, -0.0177, -0.3007],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0055, 0.0064, 0.0085, 0.0043, 0.0057], dtype=torch.float64,
requires_grad=True)
ll=-3090.897897360474, sigma_a=0.08427158703303188, pos_x_R_std=4.993275121306663, pos_y_R_std=5.11578796947727, m0=tensor([ 0.2768, -0.1308, -0.3268, -0.0598, -0.0056, -0.3092],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0065, 0.0051, 0.0053, 0.0084, 0.0040, 0.0047], dtype=torch.float64,
requires_grad=True)
ll=-3090.609780705544, sigma_a=0.08675463390086888, pos_x_R_std=4.951451521735569, pos_y_R_std=5.108883900798472, m0=tensor([ 0.4151, -0.1081, -0.3447, 0.0017, 0.1030, -0.3860],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0068, 0.0011, -0.0042, 0.0074, 0.0011, -0.0037],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 20
likelihood: -3090.609780705544
sigma_a:
tensor([0.0868], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9515], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1089], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.4151, -0.1081, -0.3447, 0.0017, 0.1030, -0.3860],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([ 0.0068, 0.0011, -0.0042, 0.0074, 0.0011, -0.0037],
dtype=torch.float64, requires_grad=True)
ll=-3090.609780705544, sigma_a=0.08675463390086888, pos_x_R_std=4.951451521735569, pos_y_R_std=5.108883900798472, m0=tensor([ 0.4151, -0.1081, -0.3447, 0.0017, 0.1030, -0.3860],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0068, 0.0011, -0.0042, 0.0074, 0.0011, -0.0037],
dtype=torch.float64, requires_grad=True)
ll=-3090.2076087752466, sigma_a=0.0845336977465065, pos_x_R_std=4.92086827667021, pos_y_R_std=5.146920300885666, m0=tensor([ 0.7355, -0.0746, -0.4192, 0.1413, 0.3425, -0.5890],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0071, -0.0079, -0.0248, 0.0051, -0.0057, -0.0222],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 21
likelihood: -3090.2076087752466
sigma_a:
tensor([0.0845], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9209], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1469], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.7355, -0.0746, -0.4192, 0.1413, 0.3425, -0.5890],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([ 0.0071, -0.0079, -0.0248, 0.0051, -0.0057, -0.0222],
dtype=torch.float64, requires_grad=True)
ll=-3090.2076087752466, sigma_a=0.0845336977465065, pos_x_R_std=4.92086827667021, pos_y_R_std=5.146920300885666, m0=tensor([ 0.7355, -0.0746, -0.4192, 0.1413, 0.3425, -0.5890],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0071, -0.0079, -0.0248, 0.0051, -0.0057, -0.0222],
dtype=torch.float64, requires_grad=True)
ll=-3090.0812401446588, sigma_a=0.08455815293239055, pos_x_R_std=4.944073360187049, pos_y_R_std=5.158458117796711, m0=tensor([ 0.8602, -0.0580, -0.4297, 0.1936, 0.4405, -0.6435],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0071, -0.0105, -0.0283, 0.0043, -0.0077, -0.0255],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 22
likelihood: -3090.0812401446588
sigma_a:
tensor([0.0846], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9441], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1585], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 0.8602, -0.0580, -0.4297, 0.1936, 0.4405, -0.6435],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([ 0.0071, -0.0105, -0.0283, 0.0043, -0.0077, -0.0255],
dtype=torch.float64, requires_grad=True)
ll=-3090.0812401446588, sigma_a=0.08455815293239055, pos_x_R_std=4.944073360187049, pos_y_R_std=5.158458117796711, m0=tensor([ 0.8602, -0.0580, -0.4297, 0.1936, 0.4405, -0.6435],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0071, -0.0105, -0.0283, 0.0043, -0.0077, -0.0255],
dtype=torch.float64, requires_grad=True)
ll=-3089.946444037464, sigma_a=0.08482298226264517, pos_x_R_std=4.986057027113701, pos_y_R_std=5.108825702545809, m0=tensor([ 1.0127, -0.0766, -0.4555, 0.2417, 0.5459, -0.6948],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0069, -0.0106, -0.0164, 0.0031, -0.0076, -0.0151],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 23
likelihood: -3089.946444037464
sigma_a:
tensor([0.0848], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9861], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1088], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.0127, -0.0766, -0.4555, 0.2417, 0.5459, -0.6948],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([ 0.0069, -0.0106, -0.0164, 0.0031, -0.0076, -0.0151],
dtype=torch.float64, requires_grad=True)
ll=-3089.946444037464, sigma_a=0.08482298226264517, pos_x_R_std=4.986057027113701, pos_y_R_std=5.108825702545809, m0=tensor([ 1.0127, -0.0766, -0.4555, 0.2417, 0.5459, -0.6948],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0069, -0.0106, -0.0164, 0.0031, -0.0076, -0.0151],
dtype=torch.float64, requires_grad=True)
ll=-3089.9046896721156, sigma_a=0.08448523500930381, pos_x_R_std=4.9959285748963564, pos_y_R_std=5.115383699355141, m0=tensor([ 1.0582, -0.1045, -0.4548, 0.2401, 0.5617, -0.6934],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0065, -0.0083, -0.0058, 0.0028, -0.0058, -0.0055],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 24
likelihood: -3089.9046896721156
sigma_a:
tensor([0.0845], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9959], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1154], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.0582, -0.1045, -0.4548, 0.2401, 0.5617, -0.6934],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([ 0.0065, -0.0083, -0.0058, 0.0028, -0.0058, -0.0055],
dtype=torch.float64, requires_grad=True)
ll=-3089.9046896721156, sigma_a=0.08448523500930381, pos_x_R_std=4.9959285748963564, pos_y_R_std=5.115383699355141, m0=tensor([ 1.0582, -0.1045, -0.4548, 0.2401, 0.5617, -0.6934],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([ 0.0065, -0.0083, -0.0058, 0.0028, -0.0058, -0.0055],
dtype=torch.float64, requires_grad=True)
ll=-3089.778420932146, sigma_a=0.0834145660354255, pos_x_R_std=5.011483568162047, pos_y_R_std=5.1321020018686605, m0=tensor([ 1.2449, -0.2410, -0.4354, 0.2133, 0.6029, -0.6752],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0049, 0.0032, 0.0377, 0.0013, 0.0029, 0.0338], dtype=torch.float64,
requires_grad=True)
--------------------------------------------------------------------------------
epoch: 25
likelihood: -3089.778420932146
sigma_a:
tensor([0.0834], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0115], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1321], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.2449, -0.2410, -0.4354, 0.2133, 0.6029, -0.6752],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([0.0049, 0.0032, 0.0377, 0.0013, 0.0029, 0.0338], dtype=torch.float64,
requires_grad=True)
ll=-3089.778420932146, sigma_a=0.0834145660354255, pos_x_R_std=5.011483568162047, pos_y_R_std=5.1321020018686605, m0=tensor([ 1.2449, -0.2410, -0.4354, 0.2133, 0.6029, -0.6752],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([0.0049, 0.0032, 0.0377, 0.0013, 0.0029, 0.0338], dtype=torch.float64,
requires_grad=True)
ll=-3089.6809255495773, sigma_a=0.08314299759985391, pos_x_R_std=5.007514983671093, pos_y_R_std=5.138193335542621, m0=tensor([ 1.4180, -0.3704, -0.4053, 0.1795, 0.6264, -0.6631],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.2588e-03, 1.3201e-02, 6.7341e-02, 5.1694e-05, 1.0497e-02, 6.0480e-02],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 26
likelihood: -3089.6809255495773
sigma_a:
tensor([0.0831], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0075], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1382], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.4180, -0.3704, -0.4053, 0.1795, 0.6264, -0.6631],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([3.2588e-03, 1.3201e-02, 6.7341e-02, 5.1694e-05, 1.0497e-02, 6.0480e-02],
dtype=torch.float64, requires_grad=True)
ll=-3089.6809255495773, sigma_a=0.08314299759985391, pos_x_R_std=5.007514983671093, pos_y_R_std=5.138193335542621, m0=tensor([ 1.4180, -0.3704, -0.4053, 0.1795, 0.6264, -0.6631],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([3.2588e-03, 1.3201e-02, 6.7341e-02, 5.1694e-05, 1.0497e-02, 6.0480e-02],
dtype=torch.float64, requires_grad=True)
ll=-3089.3670808693273, sigma_a=0.08253279501079035, pos_x_R_std=5.011455577503597, pos_y_R_std=5.144321708013158, m0=tensor([ 1.9786, -0.7677, -0.2807, 0.0609, 0.6735, -0.6619],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0017, 0.0362, 0.1103, -0.0039, 0.0288, 0.0970],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 27
likelihood: -3089.3670808693273
sigma_a:
tensor([0.0825], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0115], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1443], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 1.9786, -0.7677, -0.2807, 0.0609, 0.6735, -0.6619],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0017, 0.0362, 0.1103, -0.0039, 0.0288, 0.0970],
dtype=torch.float64, requires_grad=True)
ll=-3089.3670808693273, sigma_a=0.08253279501079035, pos_x_R_std=5.011455577503597, pos_y_R_std=5.144321708013158, m0=tensor([ 1.9786, -0.7677, -0.2807, 0.0609, 0.6735, -0.6619],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0017, 0.0362, 0.1103, -0.0039, 0.0288, 0.0970],
dtype=torch.float64, requires_grad=True)
ll=-3089.0010503089047, sigma_a=0.08108159581269377, pos_x_R_std=5.035571446414351, pos_y_R_std=5.150497599551826, m0=tensor([ 3.1888, -1.6258, -0.0458, -0.1958, 0.7887, -0.6041],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0100, 0.0137, 0.0348, -0.0103, 0.0146, 0.0154],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 28
likelihood: -3089.0010503089047
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0356], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1505], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.1888, -1.6258, -0.0458, -0.1958, 0.7887, -0.6041],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0100, 0.0137, 0.0348, -0.0103, 0.0146, 0.0154],
dtype=torch.float64, requires_grad=True)
ll=-3089.0010503089047, sigma_a=0.08108159581269377, pos_x_R_std=5.035571446414351, pos_y_R_std=5.150497599551826, m0=tensor([ 3.1888, -1.6258, -0.0458, -0.1958, 0.7887, -0.6041],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0100, 0.0137, 0.0348, -0.0103, 0.0146, 0.0154],
dtype=torch.float64, requires_grad=True)
ll=-3088.6887483704536, sigma_a=0.08083453860292061, pos_x_R_std=5.020588482196077, pos_y_R_std=5.136369735242437, m0=tensor([ 3.6836, -1.9032, 0.1440, -0.3237, 0.7567, -0.6911],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0125, 0.0096, 0.0551, -0.0121, 0.0116, 0.0464],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 29
likelihood: -3088.6887483704536
sigma_a:
tensor([0.0808], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([5.0206], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1364], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.6836, -1.9032, 0.1440, -0.3237, 0.7567, -0.6911],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0125, 0.0096, 0.0551, -0.0121, 0.0116, 0.0464],
dtype=torch.float64, requires_grad=True)
ll=-3088.6887483704536, sigma_a=0.08083453860292061, pos_x_R_std=5.020588482196077, pos_y_R_std=5.136369735242437, m0=tensor([ 3.6836, -1.9032, 0.1440, -0.3237, 0.7567, -0.6911],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0125, 0.0096, 0.0551, -0.0121, 0.0116, 0.0464],
dtype=torch.float64, requires_grad=True)
ll=-3088.6114418963793, sigma_a=0.08108657702479175, pos_x_R_std=4.994102001822459, pos_y_R_std=5.128027948677246, m0=tensor([ 3.5950, -1.8408, 0.1347, -0.3179, 0.7287, -0.6924],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0108, -0.0053, 0.0120, -0.0106, -0.0007, 0.0104],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 30
likelihood: -3088.6114418963793
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9941], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1280], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5950, -1.8408, 0.1347, -0.3179, 0.7287, -0.6924],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0108, -0.0053, 0.0120, -0.0106, -0.0007, 0.0104],
dtype=torch.float64, requires_grad=True)
ll=-3088.6114418963793, sigma_a=0.08108657702479175, pos_x_R_std=4.994102001822459, pos_y_R_std=5.128027948677246, m0=tensor([ 3.5950, -1.8408, 0.1347, -0.3179, 0.7287, -0.6924],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0108, -0.0053, 0.0120, -0.0106, -0.0007, 0.0104],
dtype=torch.float64, requires_grad=True)
ll=-3088.6042408716376, sigma_a=0.08107185245657991, pos_x_R_std=4.980295547174949, pos_y_R_std=5.12251597897683, m0=tensor([ 3.5444, -1.8132, 0.1261, -0.3153, 0.7153, -0.6856],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0098, -0.0076, -0.0006, -0.0097, -0.0034, -0.0003],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 31
likelihood: -3088.6042408716376
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9803], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1225], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5444, -1.8132, 0.1261, -0.3153, 0.7153, -0.6856],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0098, -0.0076, -0.0006, -0.0097, -0.0034, -0.0003],
dtype=torch.float64, requires_grad=True)
ll=-3088.6042408716376, sigma_a=0.08107185245657991, pos_x_R_std=4.980295547174949, pos_y_R_std=5.12251597897683, m0=tensor([ 3.5444, -1.8132, 0.1261, -0.3153, 0.7153, -0.6856],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0098, -0.0076, -0.0006, -0.0097, -0.0034, -0.0003],
dtype=torch.float64, requires_grad=True)
ll=-3088.604058276882, sigma_a=0.08112612570090375, pos_x_R_std=4.979668804145286, pos_y_R_std=5.12215840294705, m0=tensor([ 3.5461, -1.8145, 0.1265, -0.3165, 0.7148, -0.6838],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0094, -0.0055, 0.0003, -0.0093, -0.0023, 0.0003],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 32
likelihood: -3088.604058276882
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9797], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1222], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5461, -1.8145, 0.1265, -0.3165, 0.7148, -0.6838],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0094, -0.0055, 0.0003, -0.0093, -0.0023, 0.0003],
dtype=torch.float64, requires_grad=True)
ll=-3088.604058276882, sigma_a=0.08112612570090375, pos_x_R_std=4.979668804145286, pos_y_R_std=5.12215840294705, m0=tensor([ 3.5461, -1.8145, 0.1265, -0.3165, 0.7148, -0.6838],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0094, -0.0055, 0.0003, -0.0093, -0.0023, 0.0003],
dtype=torch.float64, requires_grad=True)
ll=-3088.60391568118, sigma_a=0.08112737792029248, pos_x_R_std=4.978997736380743, pos_y_R_std=5.12188687431338, m0=tensor([ 3.5513, -1.8173, 0.1267, -0.3183, 0.7142, -0.6826],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0086, -0.0021, 0.0010, -0.0085, -0.0008, 0.0008],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 33
likelihood: -3088.60391568118
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9790], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1219], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5513, -1.8173, 0.1267, -0.3183, 0.7142, -0.6826],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0086, -0.0021, 0.0010, -0.0085, -0.0008, 0.0008],
dtype=torch.float64, requires_grad=True)
ll=-3088.60391568118, sigma_a=0.08112737792029248, pos_x_R_std=4.978997736380743, pos_y_R_std=5.12188687431338, m0=tensor([ 3.5513, -1.8173, 0.1267, -0.3183, 0.7142, -0.6826],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0086, -0.0021, 0.0010, -0.0085, -0.0008, 0.0008],
dtype=torch.float64, requires_grad=True)
ll=-3088.603772464732, sigma_a=0.08112514490820592, pos_x_R_std=4.979469053423462, pos_y_R_std=5.122000406889393, m0=tensor([ 3.5514, -1.8144, 0.1253, -0.3188, 0.7129, -0.6819],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0071, 0.0008, 0.0002, -0.0071, 0.0004, 0.0001],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 34
likelihood: -3088.603772464732
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9795], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1220], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5514, -1.8144, 0.1253, -0.3188, 0.7129, -0.6819],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0071, 0.0008, 0.0002, -0.0071, 0.0004, 0.0001],
dtype=torch.float64, requires_grad=True)
ll=-3088.603772464732, sigma_a=0.08112514490820592, pos_x_R_std=4.979469053423462, pos_y_R_std=5.122000406889393, m0=tensor([ 3.5514, -1.8144, 0.1253, -0.3188, 0.7129, -0.6819],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0071, 0.0008, 0.0002, -0.0071, 0.0004, 0.0001],
dtype=torch.float64, requires_grad=True)
ll=-3088.6037009296865, sigma_a=0.08109224786594144, pos_x_R_std=4.979754922991829, pos_y_R_std=5.122186270473922, m0=tensor([ 3.5524, -1.8115, 0.1234, -0.3192, 0.7119, -0.6818],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0053, 0.0018, -0.0002, -0.0054, 0.0008, -0.0002],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 35
likelihood: -3088.6037009296865
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9798], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1222], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5524, -1.8115, 0.1234, -0.3192, 0.7119, -0.6818],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0053, 0.0018, -0.0002, -0.0054, 0.0008, -0.0002],
dtype=torch.float64, requires_grad=True)
ll=-3088.6037009296865, sigma_a=0.08109224786594144, pos_x_R_std=4.979754922991829, pos_y_R_std=5.122186270473922, m0=tensor([ 3.5524, -1.8115, 0.1234, -0.3192, 0.7119, -0.6818],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0053, 0.0018, -0.0002, -0.0054, 0.0008, -0.0002],
dtype=torch.float64, requires_grad=True)
ll=-3088.6036694371724, sigma_a=0.08107327827077322, pos_x_R_std=4.979973650800323, pos_y_R_std=5.1223066187896915, m0=tensor([ 3.5531, -1.8094, 0.1220, -0.3196, 0.7111, -0.6815],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0038, 0.0017, -0.0005, -0.0040, 0.0007, -0.0004],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 36
likelihood: -3088.6036694371724
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9800], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1223], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5531, -1.8094, 0.1220, -0.3196, 0.7111, -0.6815],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0038, 0.0017, -0.0005, -0.0040, 0.0007, -0.0004],
dtype=torch.float64, requires_grad=True)
ll=-3088.6036694371724, sigma_a=0.08107327827077322, pos_x_R_std=4.979973650800323, pos_y_R_std=5.1223066187896915, m0=tensor([ 3.5531, -1.8094, 0.1220, -0.3196, 0.7111, -0.6815],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0038, 0.0017, -0.0005, -0.0040, 0.0007, -0.0004],
dtype=torch.float64, requires_grad=True)
ll=-3088.603648768047, sigma_a=0.08106543916515904, pos_x_R_std=4.979993661870076, pos_y_R_std=5.122323495278795, m0=tensor([ 3.5540, -1.8085, 0.1212, -0.3201, 0.7104, -0.6810],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0027, 0.0011, -0.0004, -0.0029, 0.0004, -0.0004],
dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 37
likelihood: -3088.603648768047
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9800], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1223], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5540, -1.8085, 0.1212, -0.3201, 0.7104, -0.6810],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-0.0027, 0.0011, -0.0004, -0.0029, 0.0004, -0.0004],
dtype=torch.float64, requires_grad=True)
ll=-3088.603648768047, sigma_a=0.08106543916515904, pos_x_R_std=4.979993661870076, pos_y_R_std=5.122323495278795, m0=tensor([ 3.5540, -1.8085, 0.1212, -0.3201, 0.7104, -0.6810],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-0.0027, 0.0011, -0.0004, -0.0029, 0.0004, -0.0004],
dtype=torch.float64, requires_grad=True)
ll=-3088.603634683067, sigma_a=0.08107209927858218, pos_x_R_std=4.9798420831092445, pos_y_R_std=5.122250655271556, m0=tensor([ 3.5551, -1.8087, 0.1210, -0.3207, 0.7099, -0.6803],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.6544e-03, 1.8478e-04, -9.6225e-05, -1.8773e-03, 3.7386e-05,
-7.2998e-05], dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 38
likelihood: -3088.603634683067
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9798], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1223], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5551, -1.8087, 0.1210, -0.3207, 0.7099, -0.6803],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-1.6544e-03, 1.8478e-04, -9.6225e-05, -1.8773e-03, 3.7386e-05,
-7.2998e-05], dtype=torch.float64, requires_grad=True)
ll=-3088.603634683067, sigma_a=0.08107209927858218, pos_x_R_std=4.9798420831092445, pos_y_R_std=5.122250655271556, m0=tensor([ 3.5551, -1.8087, 0.1210, -0.3207, 0.7099, -0.6803],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.6544e-03, 1.8478e-04, -9.6225e-05, -1.8773e-03, 3.7386e-05,
-7.2998e-05], dtype=torch.float64, requires_grad=True)
ll=-3088.603632854425, sigma_a=0.08107649631904428, pos_x_R_std=4.979761552853667, pos_y_R_std=5.122213784027458, m0=tensor([ 3.5553, -1.8092, 0.1212, -0.3208, 0.7099, -0.6801],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.5061e-03, -1.1599e-04, 2.1197e-05, -1.7180e-03, -6.5317e-05,
4.6706e-05], dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 39
likelihood: -3088.603632854425
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9798], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1222], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5553, -1.8092, 0.1212, -0.3208, 0.7099, -0.6801],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-1.5061e-03, -1.1599e-04, 2.1197e-05, -1.7180e-03, -6.5317e-05,
4.6706e-05], dtype=torch.float64, requires_grad=True)
ll=-3088.603632854425, sigma_a=0.08107649631904428, pos_x_R_std=4.979761552853667, pos_y_R_std=5.122213784027458, m0=tensor([ 3.5553, -1.8092, 0.1212, -0.3208, 0.7099, -0.6801],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.5061e-03, -1.1599e-04, 2.1197e-05, -1.7180e-03, -6.5317e-05,
4.6706e-05], dtype=torch.float64, requires_grad=True)
ll=-3088.6036322648265, sigma_a=0.08107891426609655, pos_x_R_std=4.97973202082332, pos_y_R_std=5.12219949596513, m0=tensor([ 3.5553, -1.8094, 0.1214, -0.3208, 0.7100, -0.6801],
dtype=torch.float64, requires_grad=True), sqrt_diag_V0=tensor([-1.4664e-03, -1.8853e-04, 5.9503e-05, -1.6684e-03, -8.2147e-05,
8.4051e-05], dtype=torch.float64, requires_grad=True)
--------------------------------------------------------------------------------
epoch: 40
likelihood: -3088.6036322648265
sigma_a:
tensor([0.0811], dtype=torch.float64, requires_grad=True)
pos_x_R_std:
tensor([4.9797], dtype=torch.float64, requires_grad=True)
pos_y_R_std:
tensor([5.1222], dtype=torch.float64, requires_grad=True)
m0:
tensor([ 3.5553, -1.8094, 0.1214, -0.3208, 0.7100, -0.6801],
dtype=torch.float64, requires_grad=True)
sqrt_diag_V0:
tensor([-1.4664e-03, -1.8853e-04, 5.9503e-05, -1.6684e-03, -8.2147e-05,
8.4051e-05], dtype=torch.float64, requires_grad=True)
EM
Code for ssm.learning.em_SS_tracking
V0_0 = np.diag(sqrt_diag_V0_0**2)
R0 = np.diag(sqrt_diag_R_0)
Qe_reg_param = 1e-5
Qe_regularized = Qe + Qe_reg_param*np.eye(Qe.shape[0])
optim_res_em = ssm.learning.em_SS_tracking(
y=y, B=B, sigma_a0=sigma_a0,
Qe=Qe_regularized, Z=Z, R_0=R0, m0_0=m0_0, V0_0=V0_0,
vars_to_estimate=em_vars_to_estimate, max_iter=em_max_iter)
LogLike[0000]=-233006.621729
sigma_a=0.250000
R:
[[0.05 0. ]
[0. 0.05]]
m0:
[ 3.55529482 -1.80940282 0.12136617 -0.32078742 0.70995184 -0.68009816]
V0:
[[2.15021056e-06 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[0.00000000e+00 3.55435166e-08 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[0.00000000e+00 0.00000000e+00 3.54065931e-09 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 2.78367422e-06
0.00000000e+00 0.00000000e+00]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
6.74805723e-09 0.00000000e+00]
[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
0.00000000e+00 7.06463881e-09]]
LogLike[0001]=-3114.440657
sigma_a=0.438607
R:
[[22.26249681 0.52536519]
[ 0.52536519 23.78722176]]
m0:
[ 3.55560956 -1.80940351 0.12136611 -0.3209963 0.70995193 -0.68009812]
V0:
[[ 2.14947305e-06 -5.47427724e-12 -1.08805529e-13 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[-5.47427725e-12 3.55434578e-08 -1.61591971e-15 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[-1.08805529e-13 -1.61591971e-15 3.54065923e-09 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.78243827e-06
-1.34536233e-12 -2.81028988e-13]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.34536233e-12
6.74805512e-09 -6.12100319e-16]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 -2.81028988e-13
-6.12100319e-16 7.06463853e-09]]
LogLike[0002]=-3112.152959
sigma_a=0.436790
R:
[[24.60644699 0.61730572]
[ 0.61730572 25.7661148 ]]
m0:
[ 3.55560902 -1.80940353 0.12136611 -0.32099607 0.70995193 -0.68009812]
V0:
[[ 2.14946900e-06 -5.54293857e-12 -1.12141360e-13 9.60989268e-14
1.91778075e-16 7.23417168e-17]
[-5.54293857e-12 3.55434561e-08 -1.72903680e-15 1.30767116e-15
3.58588045e-18 1.61823270e-18]
[-1.12141360e-13 -1.72903680e-15 3.54065922e-09 4.69343371e-17
1.53973960e-19 7.90796019e-20]
[ 9.60989268e-14 1.30767116e-15 4.69343371e-17 2.78243185e-06
-1.36151540e-12 -2.89373047e-13]
[ 1.91778075e-16 3.58588041e-18 1.53973960e-19 -1.36151540e-12
6.74805506e-09 -6.54057598e-16]
[ 7.23417168e-17 1.61823270e-18 7.90796019e-20 -2.89373047e-13
-6.54057598e-16 7.06463848e-09]]
LogLike[0003]=-3112.045832
sigma_a=0.434967
R:
[[24.6905498 0.61456208]
[ 0.61456208 25.83113368]]
m0:
[ 3.55560854 -1.80940354 0.12136611 -0.32099586 0.70995194 -0.68009812]
V0:
[[ 2.14946526e-06 -5.60740347e-12 -1.15333200e-13 1.92072835e-13
3.86474766e-16 1.47084369e-16]
[-5.60740347e-12 3.55434545e-08 -1.83924035e-15 2.63524256e-15
7.28793742e-18 3.31820293e-18]
[-1.15333200e-13 -1.83924035e-15 3.54065921e-09 9.54263535e-17
3.15725230e-19 1.63593628e-19]
[ 1.92072834e-13 2.63524257e-15 9.54263535e-17 2.78242582e-06
-1.37688422e-12 -2.97435220e-13]
[ 3.86474766e-16 7.28793736e-18 3.15725230e-19 -1.37688422e-12
6.74805500e-09 -6.95196558e-16]
[ 1.47084369e-16 3.31820293e-18 1.63593628e-19 -2.97435220e-13
-6.95196558e-16 7.06463844e-09]]
LogLike[0004]=-3111.941937
sigma_a=0.433165
R:
[[24.69404668 0.61381894]
[ 0.61381894 25.83336912]]
m0:
[ 3.55560805 -1.80940356 0.12136611 -0.32099565 0.70995194 -0.68009812]
V0:
[[ 2.14946153e-06 -5.67190687e-12 -1.18533530e-13 2.87243673e-13
5.79907514e-16 2.21491907e-16]
[-5.67190687e-12 3.55434528e-08 -1.94995408e-15 3.95419575e-15
1.09730865e-17 5.01380330e-18]
[-1.18533530e-13 -1.94995408e-15 3.54065920e-09 1.43700978e-16
4.77060748e-19 2.48057625e-19]
[ 2.87243673e-13 3.95419575e-15 1.43700978e-16 2.78241980e-06
-1.39227123e-12 -3.05522313e-13]
[ 5.79907514e-16 1.09730865e-17 4.77060748e-19 -1.39227123e-12
6.74805494e-09 -7.36537616e-16]
[ 2.21491907e-16 5.01380330e-18 2.48057625e-19 -3.05522313e-13
-7.36537616e-16 7.06463839e-09]]
LogLike[0005]=-3111.837640
sigma_a=0.431360
R:
[[24.69472371 0.61331936]
[ 0.61331936 25.83356457]]
m0:
[ 3.55560757 -1.80940358 0.12136611 -0.32099544 0.70995194 -0.68009812]
V0:
[[ 2.14945780e-06 -5.73658698e-12 -1.21747369e-13 3.82418109e-13
7.73619154e-16 2.96118506e-16]
[-5.73658698e-12 3.55434512e-08 -2.06129175e-15 5.27505082e-15
1.46688585e-17 6.71682382e-18]
[-1.21747369e-13 -2.06129175e-15 3.54065919e-09 1.92117753e-16
6.39102327e-19 3.33016236e-19]
[ 3.82418109e-13 5.27505083e-15 1.92117753e-16 2.78241377e-06
-1.40770094e-12 -3.13643745e-13]
[ 7.73619154e-16 1.46688586e-17 6.39102327e-19 -1.40770094e-12
6.74805488e-09 -7.78112332e-16]
[ 2.96118506e-16 6.71682382e-18 3.33016236e-19 -3.13643745e-13
-7.78112332e-16 7.06463834e-09]]
LogLike[0006]=-3111.734686
sigma_a=0.429583
R:
[[24.6953005 0.61283113]
[ 0.61283113 25.83369419]]
m0:
[ 3.55560709 -1.80940359 0.12136611 -0.32099523 0.70995194 -0.68009812]
V0:
[[ 2.14945406e-06 -5.80145022e-12 -1.24975030e-13 4.77651400e-13
9.67721074e-16 3.71006946e-16]
[-5.80145022e-12 3.55434495e-08 -2.17326449e-15 6.59856730e-15
1.83773725e-17 8.42824525e-18]
[-1.24975030e-13 -2.17326449e-15 3.54065918e-09 2.40704434e-16
8.01943288e-19 4.18518974e-19]
[ 4.77651400e-13 6.59856731e-15 2.40704434e-16 2.78240773e-06
-1.42317452e-12 -3.21800170e-13]
[ 9.67721075e-16 1.83773725e-17 8.01943288e-19 -1.42317452e-12
6.74805482e-09 -8.19924400e-16]
[ 3.71006946e-16 8.42824525e-18 4.18518974e-19 -3.21800170e-13
-8.19924400e-16 7.06463830e-09]]
LogLike[0007]=-3111.631570
sigma_a=0.427807
R:
[[24.69586281 0.61234871]
[ 0.61234871 25.83382037]]
m0:
[ 3.5556066 -1.80940361 0.12136611 -0.32099502 0.70995194 -0.68009812]
V0:
[[ 2.14945031e-06 -5.86649520e-12 -1.28216421e-13 5.72944276e-13
1.16221234e-15 4.46156173e-16]
[-5.86649520e-12 3.55434479e-08 -2.28586886e-15 7.92473880e-15
2.20985778e-17 1.01480357e-17]
[-1.28216421e-13 -2.28586886e-15 3.54065916e-09 2.89460339e-16
9.65580595e-19 5.04564192e-19]
[ 5.72944276e-13 7.92473880e-15 2.89460339e-16 2.78240169e-06
-1.43869162e-12 -3.29991352e-13]
[ 1.16221234e-15 2.20985778e-17 9.65580595e-19 -1.43869162e-12
6.74805476e-09 -8.61972519e-16]
[ 4.46156173e-16 1.01480357e-17 5.04564192e-19 -3.29991352e-13
-8.61972519e-16 7.06463825e-09]]
LogLike[0008]=-3111.528983
sigma_a=0.426044
R:
[[24.69642162 0.61186496]
[ 0.61186496 25.83394692]]
m0:
[ 3.55560612 -1.80940363 0.12136611 -0.32099481 0.70995194 -0.68009812]
V0:
[[ 2.14944656e-06 -5.93172305e-12 -1.31471648e-13 6.68298126e-13
1.35709735e-15 5.21568848e-16]
[-5.93172305e-12 3.55434462e-08 -2.39911052e-15 9.25359538e-15
2.58326041e-17 1.18762840e-17]
[-1.31471648e-13 -2.39911052e-15 3.54065915e-09 3.38387192e-16
1.13002270e-18 5.91158129e-19]
[ 6.68298126e-13 9.25359538e-15 3.38387192e-16 2.78239563e-06
-1.45425252e-12 -3.38217554e-13]
[ 1.35709735e-15 2.58326042e-17 1.13002270e-18 -1.45425252e-12
6.74805470e-09 -9.04258798e-16]
[ 5.21568848e-16 1.18762840e-17 5.91158129e-19 -3.38217554e-13
-9.04258798e-16 7.06463820e-09]]
LogLike[0009]=-3111.424129
sigma_a=0.424247
R:
[[24.69697371 0.61138276]
[ 0.61138276 25.83407319]]
m0:
[ 3.55560564 -1.80940365 0.12136611 -0.3209946 0.70995194 -0.68009812]
V0:
[[ 2.14944281e-06 -5.99713364e-12 -1.34740720e-13 7.63712242e-13
1.55237446e-15 5.97244548e-16]
[-5.99713364e-12 3.55434445e-08 -2.51299084e-15 1.05851257e-14
2.95794304e-17 1.36129926e-17]
[-1.34740720e-13 -2.51299084e-15 3.54065914e-09 3.87484721e-16
1.29526983e-18 6.78301869e-19]
[ 7.63712242e-13 1.05851257e-14 3.87484721e-16 2.78238957e-06
-1.46985717e-12 -3.46478801e-13]
[ 1.55237446e-15 2.95794304e-17 1.29526983e-18 -1.46985717e-12
6.74805465e-09 -9.46783751e-16]
[ 5.97244548e-16 1.36129926e-17 6.78301869e-19 -3.46478801e-13
-9.46783751e-16 7.06463816e-09]]
LogLike[0010]=-3111.321747
sigma_a=0.422497
R:
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[ 0.61088951 25.83420249]]
m0:
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LogLike[0011]=-3111.219862
sigma_a=0.420760
R:
[[24.69807672 0.61040632]
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m0:
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3.71123938e-17 1.71124225e-17]
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[ 7.49403767e-16 1.71124225e-17 8.54282417e-19 -3.63108769e-13
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LogLike[0012]=-3111.117788
sigma_a=0.419023
R:
[[24.69861314 0.60992497]
[ 0.60992497 25.834456 ]]
m0:
[ 3.5556042 -1.8094037 0.12136611 -0.32099398 0.70995195 -0.68009811]
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1.79594501e-18 9.43110048e-19]
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6.74805447e-09 -1.07582174e-15]
[ 8.25880560e-16 1.88749686e-17 9.43110048e-19 -3.71476887e-13
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LogLike[0013]=-3111.016788
sigma_a=0.417309
R:
[[24.69914648 0.60944206]
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m0:
[ 3.55560372 -1.80940371 0.12136611 -0.32099378 0.70995195 -0.68009811]
V0:
[[ 2.14942773e-06 -6.26063204e-12 -1.47958027e-13 1.14598168e-12
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4.46970232e-17 2.06461635e-17]
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6.74805441e-09 -1.11932113e-15]
[ 9.02624800e-16 2.06461635e-17 1.03250292e-18 -3.79880717e-13
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LogLike[0014]=-3110.916632
sigma_a=0.415613
R:
[[24.69967054 0.60896324]
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m0:
[ 3.55560324 -1.80940373 0.12136611 -0.32099357 0.70995195 -0.68009811]
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[[ 2.14942395e-06 -6.32696760e-12 -1.51297477e-13 1.24169878e-12
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2.13382026e-18 1.12245866e-18]
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6.74805435e-09 -1.16306429e-15]
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LogLike[0015]=-3110.816513
sigma_a=0.413921
R:
[[24.70018636 0.6084877 ]
[ 0.6084877 25.83483565]]
m0:
[ 3.55560276 -1.80940375 0.1213661 -0.32099337 0.70995195 -0.68009811]
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[[ 2.14942016e-06 -6.39348650e-12 -1.54650936e-13 1.33747497e-12
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[ 1.33747497e-12 1.86311286e-14 6.85709749e-16 2.78235300e-06
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[ 2.73236954e-15 5.23333367e-17 2.30398339e-18 -1.56441630e-12
6.74805428e-09 -1.20705095e-15]
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LogLike[0016]=-3110.715310
sigma_a=0.412216
R:
[[24.70069803 0.60801171]
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m0:
[ 3.55560228 -1.80940376 0.1213661 -0.32099316 0.70995196 -0.68009811]
V0:
[[ 2.14941636e-06 -6.46018952e-12 -1.58018483e-13 1.43331093e-12
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6.74805422e-09 -1.25128280e-15]
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LogLike[0017]=-3110.615533
sigma_a=0.410538
R:
[[24.70121113 0.60753001]
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m0:
[ 3.5556018 -1.80940378 0.1213661 -0.32099296 0.70995196 -0.68009811]
V0:
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LogLike[0018]=-3110.515724
sigma_a=0.408864
R:
[[24.70171363 0.60705394]
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m0:
[ 3.55560132 -1.8094038 0.1213661 -0.32099275 0.70995196 -0.68009811]
V0:
[[ 2.14940876e-06 -6.59415461e-12 -1.64796433e-13 1.62516443e-12
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LogLike[0019]=-3110.416259
sigma_a=0.407199
R:
[[24.70221223 0.60657722]
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m0:
[ 3.55560084 -1.80940381 0.1213661 -0.32099255 0.70995196 -0.68009811]
V0:
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LogLike[0020]=-3110.317076
sigma_a=0.405543
R:
[[24.70270537 0.60610137]
[ 0.60610137 25.83547304]]
m0:
[ 3.55560037 -1.80940383 0.1213661 -0.32099234 0.70995196 -0.68009811]
V0:
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LogLike[0021]=-3110.219999
sigma_a=0.403926
R:
[[24.70319333 0.60562615]
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m0:
[ 3.55559989 -1.80940385 0.1213661 -0.32099214 0.70995196 -0.68009811]
V0:
[[ 2.14939732e-06 -6.79649722e-12 -1.75070595e-13 1.91339128e-12
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LogLike[0022]=-3110.121754
sigma_a=0.402293
R:
[[24.70366759 0.60516004]
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m0:
[ 3.55559941 -1.80940386 0.1213661 -0.32099194 0.70995197 -0.68009811]
V0:
[[ 2.14939349e-06 -6.86431462e-12 -1.78523830e-13 2.00958338e-12
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LogLike[0023]=-3110.024768
sigma_a=0.400685
R:
[[24.7041433 0.60468816]
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m0:
[ 3.55559894 -1.80940388 0.1213661 -0.32099173 0.70995197 -0.68009811]
V0:
[[ 2.14938966e-06 -6.93231852e-12 -1.81991472e-13 2.10583575e-12
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[ 1.68476391e-15 3.88363469e-17 1.95787673e-18 -4.65891543e-13
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LogLike[0024]=-3109.927580
sigma_a=0.399077
R:
[[24.70460967 0.60422127]
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m0:
[ 3.55559846 -1.8094039 0.1213661 -0.32099153 0.70995197 -0.68009811]
V0:
[[ 2.14938582e-06 -7.00050731e-12 -1.85473422e-13 2.20214590e-12
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6.74805373e-09 -1.61413413e-15]
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LogLike[0025]=-3109.830791
sigma_a=0.397479
R:
[[24.70507314 0.60375297]
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m0:
[ 3.55559799 -1.80940391 0.1213661 -0.32099133 0.70995197 -0.68009811]
V0:
[[ 2.14938198e-06 -7.06888224e-12 -1.88969792e-13 2.29851515e-12
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LogLike[0026]=-3109.733688
sigma_a=0.395879
R:
[[24.70553118 0.60328583]
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m0:
[ 3.55559751 -1.80940393 0.1213661 -0.32099113 0.70995197 -0.68009811]
V0:
[[ 2.14937814e-06 -7.13744337e-12 -1.92480609e-13 2.39494293e-12
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6.74805360e-09 -1.70736750e-15]
[ 1.92464687e-15 4.44650845e-17 2.24685314e-18 -4.92401099e-13
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LogLike[0027]=-3109.637426
sigma_a=0.394297
R:
[[24.70598694 0.60281663]
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m0:
[ 3.55559704 -1.80940395 0.1213661 -0.32099093 0.70995197 -0.68009811]
V0:
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6.74805353e-09 -1.75436914e-15]
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LogLike[0028]=-3109.541054
sigma_a=0.392717
R:
[[24.70643536 0.60235065]
[ 0.60235065 25.8365013 ]]
m0:
[ 3.55559656 -1.80940396 0.1213661 -0.32099073 0.70995197 -0.68009811]
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1.03242833e-16 4.82623553e-17]
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4.59215078e-18 2.44248890e-18]
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6.74805347e-09 -1.80162794e-15]
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LogLike[0029]=-3109.445717
sigma_a=0.391157
R:
[[24.70688054 0.60188364]
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m0:
[ 3.55559609 -1.80940398 0.1213661 -0.32099053 0.70995198 -0.68009811]
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[[ 2.14936656e-06 -7.34425136e-12 -2.03100513e-13 2.68457958e-12
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1.07251057e-16 5.01745218e-17]
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4.77409352e-18 2.54121130e-18]
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6.74805341e-09 -1.84914604e-15]
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LogLike[0030]=-3109.350328
sigma_a=0.389600
R:
[[24.70731759 0.60142083]
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m0:
[ 3.55559562 -1.80940399 0.1213661 -0.32099033 0.70995198 -0.68009811]
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1.11272468e-16 5.20956794e-17]
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4.95689179e-18 2.64053605e-18]
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6.74805334e-09 -1.89692251e-15]
[ 2.24829567e-15 5.20956794e-17 2.64053605e-18 -5.28262180e-13
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LogLike[0031]=-3109.255452
sigma_a=0.388054
R:
[[24.70775117 0.60095731]
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m0:
[ 3.55559514 -1.80940401 0.1213661 -0.32099013 0.70995198 -0.68009811]
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1.15307175e-16 5.40259070e-17]
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6.74805328e-09 -1.94495936e-15]
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LogLike[0032]=-3109.160999
sigma_a=0.386519
R:
[[24.70817901 0.60049557]
[ 0.60049557 25.83702611]]
m0:
[ 3.55559467 -1.80940403 0.1213661 -0.32098993 0.70995198 -0.6800981 ]
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[[ 2.14935494e-06 -7.55274519e-12 -2.13851933e-13 2.97474006e-12
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1.19355154e-16 5.59652056e-17]
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6.74805321e-09 -1.99325706e-15]
[ 2.41175565e-15 5.59652056e-17 2.84101130e-18 -5.46414760e-13
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LogLike[0033]=-3109.066511
sigma_a=0.384987
R:
[[24.70860145 0.60003529]
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m0:
[ 3.5555942 -1.80940404 0.1213661 -0.32098973 0.70995198 -0.6800981 ]
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[[ 2.14935106e-06 -7.62261824e-12 -2.17465095e-13 3.07157620e-12
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1.23416415e-16 5.79135935e-17]
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6.74805315e-09 -2.04181631e-15]
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LogLike[0034]=-3108.972351
sigma_a=0.383463
R:
[[24.70902051 0.59957429]
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m0:
[ 3.55559373 -1.80940406 0.1213661 -0.32098953 0.70995198 -0.6800981 ]
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[[ 2.14934717e-06 -7.69267972e-12 -2.21093038e-13 3.16847069e-12
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LogLike[0035]=-3108.878572
sigma_a=0.381948
R:
[[24.70943471 0.59911424]
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m0:
[ 3.55559326 -1.80940408 0.1213661 -0.32098933 0.70995199 -0.6800981 ]
V0:
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LogLike[0036]=-3108.783662
sigma_a=0.380419
R:
[[24.70984382 0.59865543]
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m0:
[ 3.55559279 -1.80940409 0.1213661 -0.32098913 0.70995199 -0.6800981 ]
V0:
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LogLike[0037]=-3108.689803
sigma_a=0.378910
R:
[[24.71025415 0.59819072]
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m0:
[ 3.55559232 -1.80940411 0.1213661 -0.32098894 0.70995199 -0.6800981 ]
V0:
[[ 2.14933547e-06 -7.90399939e-12 -2.32066112e-13 3.45950366e-12
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LogLike[0038]=-3108.599512
sigma_a=0.377461
R:
[[24.7106568 0.59773022]
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m0:
[ 3.55559185 -1.80940412 0.1213661 -0.32098874 0.70995199 -0.6800981 ]
V0:
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LogLike[0039]=-3108.507963
sigma_a=0.375995
R:
[[24.7110413 0.59728623]
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m0:
[ 3.55559138 -1.80940414 0.1213661 -0.32098854 0.70995199 -0.6800981 ]
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LogLike[0040]=-3108.417017
sigma_a=0.374542
R:
[[24.71142719 0.59683627]
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m0:
[ 3.55559091 -1.80940416 0.1213661 -0.32098835 0.70995199 -0.6800981 ]
V0:
[[ 2.14932372e-06 -8.11701920e-12 -2.43173266e-13 3.75105190e-12
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LogLike[0041]=-3108.326166
sigma_a=0.373093
R:
[[24.71180755 0.59638845]
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m0:
[ 3.55559044 -1.80940417 0.12136609 -0.32098815 0.70995199 -0.6800981 ]
V0:
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LogLike[0042]=-3108.232375
sigma_a=0.371600
R:
[[24.71218422 0.5959406 ]
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m0:
[ 3.55558998 -1.80940419 0.12136609 -0.32098795 0.709952 -0.6800981 ]
V0:
[[ 2.14931587e-06 -8.25996739e-12 -2.50652036e-13 3.94570041e-12
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LogLike[0043]=-3108.141156
sigma_a=0.370152
R:
[[24.71256926 0.59547818]
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m0:
[ 3.55558951 -1.80940421 0.12136609 -0.32098776 0.709952 -0.6800981 ]
V0:
[[ 2.14931193e-06 -8.33173017e-12 -2.54414329e-13 4.04311458e-12
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LogLike[0044]=-3108.050521
sigma_a=0.368716
R:
[[24.71294122 0.595027 ]
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m0:
[ 3.55558904 -1.80940422 0.12136609 -0.32098756 0.709952 -0.6800981 ]
V0:
[[ 2.14930799e-06 -8.40368322e-12 -2.58191749e-13 4.14058422e-12
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LogLike[0045]=-3107.959121
sigma_a=0.367270
R:
[[24.71330731 0.59457846]
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m0:
[ 3.55558858 -1.80940424 0.12136609 -0.32098737 0.709952 -0.6800981 ]
V0:
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LogLike[0080]=-3104.994322
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LogLike[0081]=-3104.914622
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LogLike[0100]=-3103.473697
sigma_a=0.299768
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m0:
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LogLike[0101]=-3103.401363
sigma_a=0.298730
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LogLike[0102]=-3103.328720
sigma_a=0.297690
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LogLike[0103]=-3103.259680
sigma_a=0.296702
R:
[[24.72795901 0.57041968]
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m0:
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LogLike[0104]=-3103.186688
sigma_a=0.295660
R:
[[24.72811022 0.57005317]
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m0:
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LogLike[0105]=-3103.114846
sigma_a=0.294635
R:
[[24.72826759 0.56966644]
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m0:
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LogLike[0106]=-3103.044005
sigma_a=0.293626
R:
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m0:
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LogLike[0107]=-3102.972919
sigma_a=0.292615
R:
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m0:
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LogLike[0108]=-3102.901950
sigma_a=0.291607
R:
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m0:
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LogLike[0109]=-3102.833409
sigma_a=0.290635
R:
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m0:
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LogLike[0110]=-3102.763689
sigma_a=0.289648
R:
[[24.72900312 0.56778417]
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m0:
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LogLike[0111]=-3102.693804
sigma_a=0.288659
R:
[[24.72914272 0.56741164]
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m0:
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LogLike[0112]=-3102.621604
sigma_a=0.287639
R:
[[24.72928093 0.56703763]
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m0:
[ 3.55555868 -1.80940525 0.12136607 -0.32097482 0.7099521 -0.68009806]
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[[ 2.14902763e-06 -1.37505150e-11 -5.52272737e-13 1.08985443e-11
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LogLike[0113]=-3102.552478
sigma_a=0.286664
R:
[[24.7294217 0.56665107]
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m0:
[ 3.55555826 -1.80940526 0.12136607 -0.32097464 0.7099521 -0.68009806]
V0:
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LogLike[0114]=-3102.483054
sigma_a=0.285686
R:
[[24.72955505 0.56627966]
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m0:
[ 3.55555784 -1.80940528 0.12136607 -0.32097446 0.7099521 -0.68009806]
V0:
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LogLike[0115]=-3102.415524
sigma_a=0.284736
R:
[[24.72968701 0.5659067 ]
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m0:
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V0:
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LogLike[0116]=-3102.344266
sigma_a=0.283734
R:
[[24.72981383 0.56554312]
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m0:
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V0:
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LogLike[0117]=-3102.273343
sigma_a=0.282739
R:
[[24.7299455 0.56515988]
[ 0.56515988 25.85275352]]
m0:
[ 3.55555657 -1.80940532 0.12136607 -0.32097391 0.70995211 -0.68009806]
V0:
[[ 2.14900608e-06 -1.41793570e-11 -5.76913396e-13 1.14052003e-11
2.49626396e-14 1.04108979e-14]
[-1.41793570e-11 3.55432175e-08 -1.91370497e-14 1.70213646e-13
5.13995423e-16 2.56765694e-16]
[-5.76913396e-13 -1.91370497e-14 3.54065725e-09 6.75468208e-15
2.44314645e-17 1.39021484e-17]
[ 1.14052003e-11 1.70213646e-13 6.75468208e-15 2.78168420e-06
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[ 2.49626396e-14 5.13995423e-16 2.44314645e-17 -3.42240784e-12
6.74804664e-09 -7.15517300e-15]
[ 1.04108979e-14 2.56765694e-16 1.39021484e-17 -1.46408209e-12
-7.15517300e-15 7.06463070e-09]]
LogLike[0118]=-3102.204377
sigma_a=0.281773
R:
[[24.73007499 0.56477732]
[ 0.56477732 25.85302733]]
m0:
[ 3.55555615 -1.80940533 0.12136607 -0.32097373 0.70995211 -0.68009806]
V0:
[[ 2.14900176e-06 -1.42657306e-11 -5.81894208e-13 1.15066912e-11
2.52008140e-14 1.05176520e-14]
[-1.42657306e-11 3.55432150e-08 -1.93360231e-14 1.71837713e-13
5.19252418e-16 2.59581308e-16]
[-5.81894208e-13 -1.93360231e-14 3.54065723e-09 6.82394737e-15
2.46993757e-17 1.40651707e-17]
[ 1.15066912e-11 1.71837713e-13 6.82394737e-15 2.78167722e-06
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[ 2.52008140e-14 5.19252417e-16 2.46993757e-17 -3.44301948e-12
6.74804655e-09 -7.22947925e-15]
[ 1.05176520e-14 2.59581308e-16 1.40651707e-17 -1.47667080e-12
-7.22947925e-15 7.06463061e-09]]
LogLike[0119]=-3102.137010
sigma_a=0.280830
R:
[[24.73019928 0.56440454]
[ 0.56440454 25.85329731]]
m0:
[ 3.55555573 -1.80940535 0.12136607 -0.32097355 0.70995211 -0.68009806]
V0:
[[ 2.14899742e-06 -1.43523049e-11 -5.86892545e-13 1.16082327e-11
2.54393794e-14 1.06247109e-14]
[-1.43523049e-11 3.55432124e-08 -1.95359249e-14 1.73464445e-13
5.24524198e-16 2.62408240e-16]
[-5.86892545e-13 -1.95359249e-14 3.54065721e-09 6.89341038e-15
2.49683639e-17 1.42290435e-17]
[ 1.16082327e-11 1.73464445e-13 6.89341038e-15 2.78167022e-06
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[ 2.54393794e-14 5.24524198e-16 2.49683639e-17 -3.46367894e-12
6.74804646e-09 -7.30413203e-15]
[ 1.06247109e-14 2.62408240e-16 1.42290435e-17 -1.48930375e-12
-7.30413203e-15 7.06463051e-09]]
LogLike[0120]=-3102.070815
sigma_a=0.279904
R:
[[24.73031902 0.56403998]
[ 0.56403998 25.85356439]]
m0:
[ 3.55555531 -1.80940536 0.12136607 -0.32097337 0.70995211 -0.68009806]
V0:
[[ 2.14899309e-06 -1.44390763e-11 -5.91908096e-13 1.17098238e-11
2.56783287e-14 1.07320690e-14]
[-1.44390763e-11 3.55432099e-08 -1.97367398e-14 1.75093796e-13
5.29810506e-16 2.65246300e-16]
[-5.91908096e-13 -1.97367398e-14 3.54065718e-09 6.96306764e-15
2.52384110e-17 1.43937534e-17]
[ 1.17098238e-11 1.75093796e-13 6.96306764e-15 2.78166322e-06
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[ 2.56783287e-14 5.29810505e-16 2.52384110e-17 -3.48438531e-12
6.74804637e-09 -7.37912558e-15]
[ 1.07320690e-14 2.65246300e-16 1.43937534e-17 -1.50198015e-12
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LogLike[0121]=-3102.005444
sigma_a=0.278992
R:
[[24.73043507 0.56368136]
[ 0.56368136 25.85383009]]
m0:
[ 3.55555489 -1.80940538 0.12136607 -0.32097318 0.70995212 -0.68009806]
V0:
[[ 2.14898875e-06 -1.45260421e-11 -5.96940655e-13 1.18114642e-11
2.59176574e-14 1.08397231e-14]
[-1.45260421e-11 3.55432073e-08 -1.99384577e-14 1.76725734e-13
5.35111176e-16 2.68095372e-16]
[-5.96940655e-13 -1.99384577e-14 3.54065716e-09 7.03291695e-15
2.55095059e-17 1.45592925e-17]
[ 1.18114642e-11 1.76725734e-13 7.03291695e-15 2.78165621e-06
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[ 2.59176574e-14 5.35111175e-16 2.55095059e-17 -3.50513797e-12
6.74804628e-09 -7.45445617e-15]
[ 1.08397231e-14 2.68095372e-16 1.45592925e-17 -1.51469949e-12
-7.45445617e-15 7.06463032e-09]]
LogLike[0122]=-3101.940947
sigma_a=0.278092
R:
[[24.73054812 0.56332681]
[ 0.56332681 25.85409571]]
m0:
[ 3.55555448 -1.80940539 0.12136607 -0.320973 0.70995212 -0.68009806]
V0:
[[ 2.14898440e-06 -1.46132006e-11 -6.01990099e-13 1.19131538e-11
2.61573631e-14 1.09476711e-14]
[-1.46132006e-11 3.55432047e-08 -2.01410733e-14 1.78360243e-13
5.40426116e-16 2.70955392e-16]
[-6.01990099e-13 -2.01410733e-14 3.54065713e-09 7.10295706e-15
2.57816427e-17 1.47256568e-17]
[ 1.19131538e-11 1.78360243e-13 7.10295706e-15 2.78164919e-06
-3.52593654e-12 -1.52746146e-12]
[ 2.61573631e-14 5.40426115e-16 2.57816427e-17 -3.52593654e-12
6.74804619e-09 -7.53012179e-15]
[ 1.09476711e-14 2.70955392e-16 1.47256568e-17 -1.52746146e-12
-7.53012179e-15 7.06463022e-09]]
LogLike[0123]=-3101.874703
sigma_a=0.277170
R:
[[24.73065817 0.56297656]
[ 0.56297656 25.85436102]]
m0:
[ 3.55555406 -1.8094054 0.12136607 -0.32097282 0.70995212 -0.68009806]
V0:
[[ 2.14898005e-06 -1.47005502e-11 -6.07056294e-13 1.20148920e-11
2.63974418e-14 1.10559106e-14]
[-1.47005502e-11 3.55432021e-08 -2.03445803e-14 1.79997297e-13
5.45755197e-16 2.73826275e-16]
[-6.07056294e-13 -2.03445803e-14 3.54065711e-09 7.17318626e-15
2.60548132e-17 1.48928408e-17]
[ 1.20148920e-11 1.79997297e-13 7.17318626e-15 2.78164216e-06
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[ 2.63974418e-14 5.45755197e-16 2.60548132e-17 -3.54678060e-12
6.74804610e-09 -7.60612007e-15]
[ 1.10559106e-14 2.73826275e-16 1.48928408e-17 -1.54026571e-12
-7.60612007e-15 7.06463012e-09]]
LogLike[0124]=-3101.809232
sigma_a=0.276259
R:
[[24.73076951 0.56261678]
[ 0.56261678 25.85463654]]
m0:
[ 3.55555364 -1.80940542 0.12136607 -0.32097264 0.70995212 -0.68009806]
V0:
[[ 2.14897570e-06 -1.47880968e-11 -6.12139787e-13 1.21166829e-11
2.66379115e-14 1.11644536e-14]
[-1.47880969e-11 3.55431995e-08 -2.05490087e-14 1.81637016e-13
5.51099014e-16 2.76708457e-16]
[-6.12139787e-13 -2.05490087e-14 3.54065708e-09 7.24361247e-15
2.63290588e-17 1.50608770e-17]
[ 1.21166829e-11 1.81637016e-13 7.24361247e-15 2.78163513e-06
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[ 2.66379115e-14 5.51099013e-16 2.63290588e-17 -3.56767157e-12
6.74804601e-09 -7.68246228e-15]
[ 1.11644536e-14 2.76708457e-16 1.50608770e-17 -1.55311362e-12
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LogLike[0125]=-3101.742996
sigma_a=0.275338
R:
[[24.73087816 0.56226034]
[ 0.56226034 25.85491258]]
m0:
[ 3.55555323 -1.80940543 0.12136607 -0.32097246 0.70995212 -0.68009806]
V0:
[[ 2.14897134e-06 -1.48758391e-11 -6.17240469e-13 1.22185236e-11
2.68787631e-14 1.12732954e-14]
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5.56457335e-16 2.79601805e-16]
[-6.17240469e-13 -2.07543540e-14 3.54065706e-09 7.31423265e-15
2.66043670e-17 1.52297575e-17]
[ 1.22185236e-11 1.83279341e-13 7.31423265e-15 2.78162809e-06
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[ 2.68787631e-14 5.56457335e-16 2.66043670e-17 -3.58860911e-12
6.74804591e-09 -7.75914662e-15]
[ 1.12732954e-14 2.79601805e-16 1.52297575e-17 -1.56600492e-12
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LogLike[0126]=-3101.677046
sigma_a=0.274423
R:
[[24.73098647 0.56189951]
[ 0.56189951 25.85519516]]
m0:
[ 3.55555281 -1.80940544 0.12136607 -0.32097228 0.70995212 -0.68009806]
V0:
[[ 2.14896697e-06 -1.49637801e-11 -6.22358637e-13 1.23204162e-11
2.71200063e-14 1.13824428e-14]
[-1.49637801e-11 3.55431943e-08 -2.09606329e-14 1.84924335e-13
5.61830481e-16 2.82506558e-16]
[-6.22358637e-13 -2.09606329e-14 3.54065703e-09 7.38505105e-15
2.68807605e-17 1.53995008e-17]
[ 1.23204162e-11 1.84924335e-13 7.38505105e-15 2.78162104e-06
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[ 2.71200063e-14 5.61830480e-16 2.68807605e-17 -3.60959397e-12
6.74804582e-09 -7.83617939e-15]
[ 1.13824428e-14 2.82506558e-16 1.53995008e-17 -1.57894036e-12
-7.83617939e-15 7.06462983e-09]]
LogLike[0127]=-3101.613153
sigma_a=0.273537
R:
[[24.73109283 0.56153959]
[ 0.56153959 25.85548019]]
m0:
[ 3.5555524 -1.80940546 0.12136607 -0.32097209 0.70995212 -0.68009806]
V0:
[[ 2.14896260e-06 -1.50519199e-11 -6.27494313e-13 1.24223598e-11
2.73616383e-14 1.14918946e-14]
[-1.50519199e-11 3.55431916e-08 -2.11678475e-14 1.86571981e-13
5.67218405e-16 2.85422708e-16]
[-6.27494313e-13 -2.11678475e-14 3.54065701e-09 7.45606708e-15
2.71582384e-17 1.55701076e-17]
[ 1.24223598e-11 1.86571981e-13 7.45606708e-15 2.78161398e-06
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[ 2.73616383e-14 5.67218405e-16 2.71582384e-17 -3.63062615e-12
6.74804573e-09 -7.91356138e-15]
[ 1.14918946e-14 2.85422708e-16 1.55701076e-17 -1.59191998e-12
-7.91356138e-15 7.06462973e-09]]
LogLike[0128]=-3101.547779
sigma_a=0.272632
R:
[[24.73119452 0.56119014]
[ 0.56119014 25.85576 ]]
m0:
[ 3.55555199 -1.80940547 0.12136607 -0.32097191 0.70995213 -0.68009806]
V0:
[[ 2.14895822e-06 -1.51402531e-11 -6.32647041e-13 1.25243509e-11
2.76036439e-14 1.16016409e-14]
[-1.51402531e-11 3.55431890e-08 -2.13759747e-14 1.88222175e-13
5.72620619e-16 2.88349912e-16]
[-6.32647041e-13 -2.13759747e-14 3.54065698e-09 7.52727421e-15
2.74367683e-17 1.57415540e-17]
[ 1.25243509e-11 1.88222175e-13 7.52727421e-15 2.78160691e-06
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[ 2.76036439e-14 5.72620619e-16 2.74367683e-17 -3.65170436e-12
6.74804564e-09 -7.99128391e-15]
[ 1.16016409e-14 2.88349912e-16 1.57415540e-17 -1.60494264e-12
-7.99128391e-15 7.06462963e-09]]
LogLike[0129]=-3101.482532
sigma_a=0.271730
R:
[[24.73129689 0.56083269]
[ 0.56083269 25.85604936]]
m0:
[ 3.55555157 -1.80940548 0.12136607 -0.32097173 0.70995213 -0.68009805]
V0:
[[ 2.14895384e-06 -1.52287850e-11 -6.37817315e-13 1.26263944e-11
2.78460419e-14 1.17116939e-14]
[-1.52287850e-11 3.55431864e-08 -2.15850417e-14 1.89875045e-13
5.78037715e-16 2.91288596e-16]
[-6.37817315e-13 -2.15850417e-14 3.54065696e-09 7.59868032e-15
2.77163905e-17 1.59138713e-17]
[ 1.26263944e-11 1.89875045e-13 7.59868032e-15 2.78159984e-06
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[ 2.78460419e-14 5.78037715e-16 2.77163905e-17 -3.67282987e-12
6.74804554e-09 -8.06935717e-15]
[ 1.17116939e-14 2.91288596e-16 1.59138713e-17 -1.61800959e-12
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LogLike[0130]=-3101.419558
sigma_a=0.270860
R:
[[24.73139767 0.56047521]
[ 0.56047521 25.85634197]]
m0:
[ 3.55555116 -1.8094055 0.12136607 -0.32097155 0.70995213 -0.68009805]
V0:
[[ 2.14894946e-06 -1.53175161e-11 -6.43005195e-13 1.27284888e-11
2.80888293e-14 1.18220527e-14]
[-1.53175161e-11 3.55431837e-08 -2.17950530e-14 1.91530571e-13
5.83469654e-16 2.94238765e-16]
[-6.43005195e-13 -2.17950530e-14 3.54065693e-09 7.67028495e-15
2.79971056e-17 1.60870618e-17]
[ 1.27284888e-11 1.91530571e-13 7.67028495e-15 2.78159275e-06
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[ 2.80888293e-14 5.83469654e-16 2.79971056e-17 -3.69400281e-12
6.74804545e-09 -8.14778279e-15]
[ 1.18220527e-14 2.94238765e-16 1.60870618e-17 -1.63112097e-12
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LogLike[0131]=-3101.352265
sigma_a=0.269932
R:
[[24.73149365 0.56012937]
[ 0.56012937 25.85662816]]
m0:
[ 3.55555075 -1.80940551 0.12136607 -0.32097137 0.70995213 -0.68009805]
V0:
[[ 2.14894507e-06 -1.54064403e-11 -6.48210160e-13 1.28306301e-11
2.83319885e-14 1.19327056e-14]
[-1.54064404e-11 3.55431810e-08 -2.20059816e-14 1.93188633e-13
5.88915868e-16 2.97200017e-16]
[-6.48210160e-13 -2.20059816e-14 3.54065691e-09 7.74208050e-15
2.82788754e-17 1.62610970e-17]
[ 1.28306301e-11 1.93188633e-13 7.74208050e-15 2.78158566e-06
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[ 2.83319885e-14 5.88915869e-16 2.82788754e-17 -3.71522169e-12
6.74804535e-09 -8.22655071e-15]
[ 1.19327056e-14 2.97200017e-16 1.62610970e-17 -1.64427546e-12
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LogLike[0132]=-3101.287831
sigma_a=0.269044
R:
[[24.73159434 0.55976038]
[ 0.55976038 25.85693686]]
m0:
[ 3.55555034 -1.80940553 0.12136607 -0.32097118 0.70995213 -0.68009805]
V0:
[[ 2.14894067e-06 -1.54955719e-11 -6.53433487e-13 1.29328288e-11
2.85755630e-14 1.20436820e-14]
[-1.54955719e-11 3.55431784e-08 -2.22178969e-14 1.94849526e-13
5.94377779e-16 3.00173385e-16]
[-6.53433487e-13 -2.22178969e-14 3.54065688e-09 7.81408593e-15
2.85617981e-17 1.64360531e-17]
[ 1.29328288e-11 1.94849526e-13 7.81408593e-15 2.78157857e-06
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[ 2.85755630e-14 5.94377780e-16 2.85617981e-17 -3.73648992e-12
6.74804526e-09 -8.30568688e-15]
[ 1.20436820e-14 3.00173385e-16 1.64360531e-17 -1.65747630e-12
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LogLike[0133]=-3101.225506
sigma_a=0.268186
R:
[[24.73168965 0.55940548]
[ 0.55940548 25.85723716]]
m0:
[ 3.55554993 -1.80940554 0.12136607 -0.320971 0.70995213 -0.68009805]
V0:
[[ 2.14893627e-06 -1.55849028e-11 -6.58674492e-13 1.30350754e-11
2.88195199e-14 1.21549620e-14]
[-1.55849028e-11 3.55431757e-08 -2.24307635e-14 1.96513027e-13
5.99854421e-16 3.03158222e-16]
[-6.58674492e-13 -2.24307635e-14 3.54065685e-09 7.88628839e-15
2.88458122e-17 1.66118856e-17]
[ 1.30350754e-11 1.96513027e-13 7.88628839e-15 2.78157146e-06
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[ 2.88195199e-14 5.99854421e-16 2.88458122e-17 -3.75780558e-12
6.74804517e-09 -8.38517800e-15]
[ 1.21549620e-14 3.03158222e-16 1.66118856e-17 -1.67072174e-12
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LogLike[0134]=-3101.161981
sigma_a=0.267312
R:
[[24.73178045 0.55906179]
[ 0.55906179 25.8575311 ]]
m0:
[ 3.55554952 -1.80940555 0.12136607 -0.32097082 0.70995214 -0.68009805]
V0:
[[ 2.14893187e-06 -1.56744273e-11 -6.63932690e-13 1.31373689e-11
2.90638491e-14 1.22665376e-14]
[-1.56744273e-11 3.55431730e-08 -2.26445563e-14 1.98179068e-13
6.05345402e-16 3.06154231e-16]
[-6.63932690e-13 -2.26445563e-14 3.54065683e-09 7.95868264e-15
2.91308894e-17 1.67885724e-17]
[ 1.31373689e-11 1.98179068e-13 7.95868264e-15 2.78156435e-06
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6.74804507e-09 -8.46501469e-15]
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LogLike[0135]=-3101.100620
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LogLike[0136]=-3101.036580
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LogLike[0137]=-3100.975285
sigma_a=0.264750
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LogLike[0138]=-3100.910613
sigma_a=0.263865
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m0:
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LogLike[0139]=-3100.849083
sigma_a=0.263024
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[[24.7322183 0.55732035]
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m0:
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LogLike[0140]=-3100.787942
sigma_a=0.262188
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m0:
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LogLike[0141]=-3100.727169
sigma_a=0.261359
R:
[[24.73238003 0.55663791]
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m0:
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LogLike[0142]=-3100.665602
sigma_a=0.260520
R:
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m0:
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LogLike[0143]=-3100.604855
sigma_a=0.259692
R:
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m0:
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LogLike[0144]=-3100.543150
sigma_a=0.258853
R:
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m0:
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LogLike[0145]=-3100.480465
sigma_a=0.258001
R:
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m0:
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LogLike[0146]=-3100.421336
sigma_a=0.257198
R:
[[24.73276289 0.55492049]
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m0:
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LogLike[0147]=-3100.361685
sigma_a=0.256389
R:
[[24.73283313 0.55458816]
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m0:
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LogLike[0148]=-3100.301511
sigma_a=0.255573
R:
[[24.73290265 0.55425307]
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m0:
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LogLike[0149]=-3100.240111
sigma_a=0.254742
R:
[[24.73297161 0.55391456]
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m0:
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V0:
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LogLike[0150]=-3100.181460
sigma_a=0.253949
R:
[[24.73304073 0.55356875]
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m0:
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LogLike[0151]=-3100.122629
sigma_a=0.253154
R:
[[24.7331058 0.55323729]
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m0:
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LogLike[0152]=-3100.062307
sigma_a=0.252340
R:
[[24.73316985 0.55290478]
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m0:
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V0:
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6.74804331e-09 -9.96337345e-15]
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LogLike[0153]=-3100.000912
sigma_a=0.251512
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[[24.73323431 0.55256356]
[ 0.55256356 25.86370974]]
m0:
[ 3.55554191 -1.8094058 0.12136607 -0.32096736 0.70995216 -0.68009804]
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7.12491645e-16 3.65285701e-16]
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3.47573978e-17 2.03155472e-17]
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6.74804321e-09 -1.00500924e-14]
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LogLike[0154]=-3099.943974
sigma_a=0.250745
R:
[[24.73329875 0.55221567]
[ 0.55221567 25.86407583]]
m0:
[ 3.55554151 -1.80940581 0.12136607 -0.32096717 0.70995217 -0.68009804]
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[[ 2.14884273e-06 -1.75065294e-11 -7.72820968e-13 1.51936463e-11
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7.18281339e-16 3.68516630e-16]
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3.50648295e-17 2.05103898e-17]
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6.74804311e-09 -1.01371990e-14]
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LogLike[0155]=-3099.886895
sigma_a=0.249977
R:
[[24.73335768 0.55189164]
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m0:
[ 3.55554112 -1.80940583 0.12136607 -0.32096699 0.70995217 -0.68009804]
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[[ 2.14883822e-06 -1.76002296e-11 -7.78454411e-13 1.52969739e-11
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7.24085375e-16 3.71759032e-16]
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3.53733530e-17 2.07061322e-17]
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6.74804300e-09 -1.02246675e-14]
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LogLike[0156]=-3099.829885
sigma_a=0.249210
R:
[[24.73341555 0.55156705]
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m0:
[ 3.55554073 -1.80940584 0.12136607 -0.32096681 0.70995217 -0.68009804]
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[[ 2.14883371e-06 -1.76941248e-11 -7.84105599e-13 1.54003491e-11
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3.56829918e-17 2.09027918e-17]
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6.74804290e-09 -1.03125011e-14]
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LogLike[0157]=-3099.772170
sigma_a=0.248434
R:
[[24.73347234 0.55124237]
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m0:
[ 3.55554034 -1.80940585 0.12136607 -0.32096663 0.70995217 -0.68009804]
V0:
[[ 2.14882919e-06 -1.77882158e-11 -7.89774605e-13 1.55037718e-11
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7.35737637e-16 3.78279047e-16]
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3.59937506e-17 2.11003736e-17]
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[ 3.47894711e-14 7.35737637e-16 3.59937506e-17 -4.28350205e-12
6.74804280e-09 -1.04007016e-14]
[ 1.49172687e-14 3.78279047e-16 2.11003736e-17 -2.00202342e-12
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LogLike[0158]=-3099.717387
sigma_a=0.247699
R:
[[24.73352877 0.55091329]
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m0:
[ 3.55553995 -1.80940586 0.12136607 -0.32096644 0.70995217 -0.68009804]
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[[ 2.14882467e-06 -1.78825058e-11 -7.95461746e-13 1.56072437e-11
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6.74804270e-09 -1.04892759e-14]
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LogLike[0159]=-3099.657764
sigma_a=0.246899
R:
[[24.73358152 0.55059987]
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m0:
[ 3.55553956 -1.80940588 0.12136607 -0.32096626 0.70995217 -0.68009804]
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[[ 2.14882014e-06 -1.79769855e-11 -8.01166190e-13 1.57107584e-11
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7.47448926e-16 3.84846198e-16]
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[ 1.57107584e-11 2.40681323e-13 9.83313089e-15 2.78138393e-06
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LogLike[0160]=-3099.603004
sigma_a=0.246166
R:
[[24.73363758 0.55025955]
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m0:
[ 3.55553917 -1.80940589 0.12136607 -0.32096608 0.70995218 -0.68009804]
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[[ 2.14881561e-06 -1.80716724e-11 -8.06889570e-13 1.58143286e-11
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LogLike[0161]=-3099.543649
sigma_a=0.245371
R:
[[24.73368846 0.54994499]
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m0:
[ 3.55553879 -1.8094059 0.12136607 -0.32096589 0.70995218 -0.68009804]
V0:
[[ 2.14881107e-06 -1.81665506e-11 -8.12630434e-13 1.59179398e-11
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LogLike[0162]=-3099.487538
sigma_a=0.244620
R:
[[24.73374222 0.5496051 ]
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m0:
[ 3.5555384 -1.80940591 0.12136607 -0.32096571 0.70995218 -0.68009804]
V0:
[[ 2.14880653e-06 -1.82616370e-11 -8.18390353e-13 1.60216062e-11
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7.65129284e-16 3.94787611e-16]
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[ 3.60609223e-14 7.65129284e-16 3.75645877e-17 -4.39644427e-12
6.74804228e-09 -1.08472924e-14]
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LogLike[0163]=-3099.432038
sigma_a=0.243878
R:
[[24.73379233 0.54928204]
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m0:
[ 3.55553801 -1.80940593 0.12136607 -0.32096553 0.70995218 -0.68009803]
V0:
[[ 2.14880198e-06 -1.83569211e-11 -8.24168383e-13 1.61253168e-11
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[ 1.61253168e-11 2.47632897e-13 1.01446467e-14 2.78135461e-06
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[ 3.63163586e-14 7.71052893e-16 3.78822094e-17 -4.41917519e-12
6.74804218e-09 -1.09377462e-14]
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LogLike[0164]=-3099.379193
sigma_a=0.243173
R:
[[24.73384087 0.54896241]
[ 0.54896241 25.86768717]]
m0:
[ 3.55553763 -1.80940594 0.12136607 -0.32096534 0.70995218 -0.68009803]
V0:
[[ 2.14879743e-06 -1.84524015e-11 -8.29964422e-13 1.62290738e-11
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7.76991316e-16 4.01475550e-16]
[-8.29964422e-13 -2.95098728e-14 3.54065597e-09 1.02230265e-14
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[ 1.62290738e-11 2.49377234e-13 1.02230265e-14 2.78134726e-06
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[ 3.65721684e-14 7.76991316e-16 3.82009647e-17 -4.44195275e-12
6.74804207e-09 -1.10285734e-14]
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LogLike[0165]=-3099.324130
sigma_a=0.242438
R:
[[24.73388631 0.54865726]
[ 0.54865726 25.86804383]]
m0:
[ 3.55553725 -1.80940595 0.12136607 -0.32096516 0.70995218 -0.68009803]
V0:
[[ 2.14879287e-06 -1.85480697e-11 -8.35777697e-13 1.63328729e-11
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[-1.85480697e-11 3.55430850e-08 -2.97540867e-14 2.51123966e-13
7.82943778e-16 4.04836807e-16]
[-8.35777697e-13 -2.97540867e-14 3.54065594e-09 1.03015942e-14
3.85207982e-17 2.27148910e-17]
[ 1.63328729e-11 2.51123966e-13 1.03015942e-14 2.78133990e-06
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[ 3.68283295e-14 7.82943778e-16 3.85207982e-17 -4.46477487e-12
6.74804197e-09 -1.11197586e-14]
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LogLike[0166]=-3099.271904
sigma_a=0.241741
R:
[[24.7339326 0.54833964]
[ 0.54833964 25.86841837]]
m0:
[ 3.55553686 -1.80940596 0.12136607 -0.32096497 0.70995218 -0.68009803]
V0:
[[ 2.14878831e-06 -1.86439342e-11 -8.41609026e-13 1.64367213e-11
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LogLike[0167]=-3099.216682
sigma_a=0.241006
R:
[[24.73397584 0.54803712]
[ 0.54803712 25.86877835]]
m0:
[ 3.55553648 -1.80940597 0.12136607 -0.32096479 0.70995219 -0.68009803]
V0:
[[ 2.14878374e-06 -1.87399859e-11 -8.47457565e-13 1.65406113e-11
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[-8.47457565e-13 -3.02454866e-14 3.54065588e-09 1.04593184e-14
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6.74804176e-09 -1.13032372e-14]
[ 1.61205275e-14 4.11594747e-16 2.31278779e-17 -2.14777725e-12
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LogLike[0168]=-3099.162756
sigma_a=0.240288
R:
[[24.7340205 0.54771774]
[ 0.54771774 25.86916176]]
m0:
[ 3.5555361 -1.80940599 0.12136607 -0.32096461 0.70995219 -0.68009803]
V0:
[[ 2.14877917e-06 -1.88362361e-11 -8.53324398e-13 1.66445521e-11
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3.94870601e-17 2.33357897e-17]
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6.74804165e-09 -1.13955365e-14]
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LogLike[0169]=-3099.109441
sigma_a=0.239579
R:
[[24.73406338 0.54740466]
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m0:
[ 3.55553572 -1.809406 0.12136607 -0.32096442 0.70995219 -0.68009803]
V0:
[[ 2.14877460e-06 -1.89326812e-11 -8.59209191e-13 1.67485378e-11
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8.06900834e-16 4.18400395e-16]
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3.98114146e-17 2.35446454e-17]
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6.74804155e-09 -1.14882101e-14]
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LogLike[0170]=-3099.052555
sigma_a=0.238822
R:
[[24.73410493 0.54709474]
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m0:
[ 3.55553534 -1.80940601 0.12136607 -0.32096424 0.70995219 -0.68009803]
V0:
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LogLike[0172]=-3098.946369
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LogLike[0173]=-3098.891011
sigma_a=0.236679
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LogLike[0174]=-3098.837766
sigma_a=0.235973
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m0:
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LogLike[0175]=-3098.780985
sigma_a=0.235221
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m0:
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LogLike[0176]=-3098.730013
sigma_a=0.234547
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m0:
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LogLike[0177]=-3098.680744
sigma_a=0.233895
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m0:
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LogLike[0178]=-3098.628147
sigma_a=0.233200
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m0:
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LogLike[0179]=-3098.575475
sigma_a=0.232505
R:
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m0:
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LogLike[0180]=-3098.524574
sigma_a=0.231833
R:
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m0:
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LogLike[0181]=-3098.471648
sigma_a=0.231135
R:
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m0:
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LogLike[0182]=-3098.420699
sigma_a=0.230464
R:
[[24.73453979 0.54334905]
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m0:
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LogLike[0183]=-3098.369140
sigma_a=0.229785
R:
[[24.73456982 0.54304621]
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m0:
[ 3.55553048 -1.80940617 0.12136607 -0.32096183 0.70995221 -0.68009802]
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LogLike[0184]=-3098.317048
sigma_a=0.229099
R:
[[24.73459944 0.54273966]
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m0:
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LogLike[0185]=-3098.266120
sigma_a=0.228430
R:
[[24.73462867 0.5424294 ]
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m0:
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LogLike[0186]=-3098.213391
sigma_a=0.227737
R:
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m0:
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LogLike[0187]=-3098.164803
sigma_a=0.227098
R:
[[24.73468478 0.5418103 ]
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m0:
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V0:
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LogLike[0188]=-3098.113501
sigma_a=0.226425
R:
[[24.73471026 0.54151859]
[ 0.54151859 25.87732848]]
m0:
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[[ 2.14868677e-06 -2.08027757e-11 -9.74515136e-13 1.87330852e-11
4.28232992e-14 1.87475534e-14]
[-2.08027757e-11 3.55430141e-08 -3.56535132e-14 2.92003074e-13
9.23977396e-16 4.85494142e-16]
[-9.74515136e-13 -3.56535132e-14 3.54065519e-09 1.21638759e-14
4.61956039e-17 2.76989524e-17]
[ 1.87330852e-11 2.92003074e-13 1.21638759e-14 2.78116863e-06
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[ 4.28232992e-14 9.23977395e-16 4.61956039e-17 -5.00258174e-12
6.74803947e-09 -1.33223605e-14]
[ 1.87475534e-14 4.85494142e-16 2.76989524e-17 -2.46878860e-12
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LogLike[0189]=-3098.065909
sigma_a=0.225801
R:
[[24.73473634 0.54121125]
[ 0.54121125 25.87777069]]
m0:
[ 3.55552828 -1.80940624 0.12136607 -0.32096071 0.70995222 -0.68009802]
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[[ 2.14868210e-06 -2.09031839e-11 -9.80769248e-13 1.88379935e-11
4.30884393e-14 1.88760744e-14]
[-2.09031839e-11 3.55430109e-08 -3.59225801e-14 2.93811044e-13
9.30290357e-16 4.89149199e-16]
[-9.80769248e-13 -3.59225801e-14 3.54065515e-09 1.22472679e-14
4.65433952e-17 2.79275702e-17]
[ 1.88379935e-11 2.93811044e-13 1.22472679e-14 2.78116109e-06
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[ 4.30884393e-14 9.30290357e-16 4.65433952e-17 -5.02652867e-12
6.74803936e-09 -1.34228124e-14]
[ 1.88760744e-14 4.89149199e-16 2.79275702e-17 -2.48458818e-12
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LogLike[0190]=-3098.020306
sigma_a=0.225203
R:
[[24.7347601 0.5409246 ]
[ 0.5409246 25.87818647]]
m0:
[ 3.55552792 -1.80940625 0.12136607 -0.32096052 0.70995222 -0.68009802]
V0:
[[ 2.14867742e-06 -2.10037801e-11 -9.87041067e-13 1.89429404e-11
4.33539218e-14 1.90048864e-14]
[-2.10037801e-11 3.55430076e-08 -3.61926576e-14 2.95621348e-13
9.36617427e-16 4.92815968e-16]
[-9.87041067e-13 -3.61926576e-14 3.54065512e-09 1.23308488e-14
4.68923011e-17 2.81571407e-17]
[ 1.89429404e-11 2.95621348e-13 1.23308488e-14 2.78115354e-06
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[ 4.33539218e-14 9.36617427e-16 4.68923011e-17 -5.05052016e-12
6.74803924e-09 -1.35236410e-14]
[ 1.90048864e-14 4.92815968e-16 2.81571407e-17 -2.50043234e-12
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LogLike[0191]=-3097.970496
sigma_a=0.224550
R:
[[24.73478232 0.54064972]
[ 0.54064972 25.87858819]]
m0:
[ 3.55552756 -1.80940626 0.12136607 -0.32096033 0.70995222 -0.68009802]
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[[ 2.14867274e-06 -2.11045571e-11 -9.93329935e-13 1.90479260e-11
4.36197374e-14 1.91339804e-14]
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9.42958173e-16 4.96494075e-16]
[-9.93329935e-13 -3.64637091e-14 3.54065508e-09 1.24146127e-14
4.72422859e-17 2.83876333e-17]
[ 1.90479260e-11 2.97433925e-13 1.24146127e-14 2.78114599e-06
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[ 4.36197374e-14 9.42958173e-16 4.72422859e-17 -5.07455452e-12
6.74803913e-09 -1.36248326e-14]
[ 1.91339804e-14 4.96494075e-16 2.83876333e-17 -2.51631944e-12
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LogLike[0192]=-3097.919860
sigma_a=0.223887
R:
[[24.73480584 0.54035008]
[ 0.54035008 25.87902941]]
m0:
[ 3.5555272 -1.80940627 0.12136607 -0.32096015 0.70995223 -0.68009802]
V0:
[[ 2.14866806e-06 -2.12055326e-11 -9.99637521e-13 1.91529619e-11
4.38859381e-14 1.92633935e-14]
[-2.12055326e-11 3.55430012e-08 -3.67358309e-14 2.99249127e-13
9.49314393e-16 5.00184907e-16]
[-9.99637521e-13 -3.67358309e-14 3.54065505e-09 1.24985837e-14
4.75934817e-17 2.86191566e-17]
[ 1.91529619e-11 2.99249127e-13 1.24985837e-14 2.78113844e-06
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[ 4.38859381e-14 9.49314393e-16 4.75934817e-17 -5.09863592e-12
6.74803902e-09 -1.37264232e-14]
[ 1.92633935e-14 5.00184907e-16 2.86191566e-17 -2.53225369e-12
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LogLike[0193]=-3097.870074
sigma_a=0.223236
R:
[[24.73482922 0.54004436]
[ 0.54004436 25.87948324]]
m0:
[ 3.55552684 -1.80940628 0.12136607 -0.32095996 0.70995223 -0.68009801]
V0:
[[ 2.14866337e-06 -2.13067105e-11 -1.00596424e-12 1.92580450e-11
4.41525216e-14 1.93931276e-14]
[-2.13067105e-11 3.55429980e-08 -3.70090476e-14 3.01066941e-13
9.55686183e-16 5.03888612e-16]
[-1.00596424e-12 -3.70090476e-14 3.54065501e-09 1.25827631e-14
4.79459024e-17 2.88517262e-17]
[ 1.92580450e-11 3.01066941e-13 1.25827631e-14 2.78113087e-06
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[ 4.41525216e-14 9.55686183e-16 4.79459024e-17 -5.12276535e-12
6.74803890e-09 -1.38284220e-14]
[ 1.93931276e-14 5.03888612e-16 2.88517262e-17 -2.54823612e-12
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LogLike[0194]=-3097.821270
sigma_a=0.222597
R:
[[24.73485166 0.53974293]
[ 0.53974293 25.87993433]]
m0:
[ 3.55552648 -1.80940629 0.12136607 -0.32095977 0.70995223 -0.68009801]
V0:
[[ 2.14865868e-06 -2.14080885e-11 -1.01230987e-12 1.93631726e-11
4.44194776e-14 1.95231763e-14]
[-2.14080885e-11 3.55429947e-08 -3.72833477e-14 3.02887295e-13
9.62073224e-16 5.07604972e-16]
[-1.01230987e-12 -3.72833477e-14 3.54065498e-09 1.26671467e-14
4.82995274e-17 2.90853268e-17]
[ 1.93631726e-11 3.02887295e-13 1.26671467e-14 2.78112330e-06
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[ 4.44194776e-14 9.62073223e-16 4.82995274e-17 -5.14694219e-12
6.74803879e-09 -1.39308246e-14]
[ 1.95231763e-14 5.07604972e-16 2.90853268e-17 -2.56426617e-12
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LogLike[0195]=-3097.769854
sigma_a=0.221925
R:
[[24.73487309 0.53944687]
[ 0.53944687 25.88038089]]
m0:
[ 3.55552612 -1.8094063 0.12136607 -0.32095958 0.70995223 -0.68009801]
V0:
[[ 2.14865398e-06 -2.15096633e-11 -1.01867413e-12 1.94683437e-11
4.46867994e-14 1.96535344e-14]
[-2.15096634e-11 3.55429915e-08 -3.75587164e-14 3.04710144e-13
9.68475268e-16 5.11333799e-16]
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4.86543387e-17 2.93199442e-17]
[ 1.94683437e-11 3.04710144e-13 1.27517310e-14 2.78111572e-06
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[ 4.46867994e-14 9.68475268e-16 4.86543387e-17 -5.17116568e-12
6.74803867e-09 -1.40336255e-14]
[ 1.96535344e-14 5.11333799e-16 2.93199442e-17 -2.58034314e-12
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LogLike[0196]=-3097.721833
sigma_a=0.221298
R:
[[24.73489499 0.53913502]
[ 0.53913502 25.88085502]]
m0:
[ 3.55552576 -1.80940631 0.12136607 -0.32095939 0.70995223 -0.68009801]
V0:
[[ 2.14864928e-06 -2.16114466e-11 -1.02505813e-12 1.95735651e-11
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9.74893467e-16 5.15075993e-16]
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[ 1.95735651e-11 3.06535708e-13 1.28365316e-14 2.78110813e-06
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[ 4.49545194e-14 9.74893466e-16 4.90104221e-17 -5.19543858e-12
6.74803856e-09 -1.41368488e-14]
[ 1.97842257e-14 5.15075993e-16 2.95556502e-17 -2.59646983e-12
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LogLike[0197]=-3097.674419
sigma_a=0.220678
R:
[[24.73491502 0.53884223]
[ 0.53884223 25.88130375]]
m0:
[ 3.5555254 -1.80940633 0.12136607 -0.32095921 0.70995223 -0.68009801]
V0:
[[ 2.14864457e-06 -2.17134252e-11 -1.03146066e-12 1.96788265e-11
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9.81326394e-16 5.18830495e-16]
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4.93676766e-17 2.97923641e-17]
[ 1.96788265e-11 3.08363696e-13 1.29215294e-14 2.78110053e-06
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[ 4.52225946e-14 9.81326394e-16 4.93676766e-17 -5.21975777e-12
6.74803844e-09 -1.42404689e-14]
[ 1.99152208e-14 5.18830495e-16 2.97923641e-17 -2.61264317e-12
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LogLike[0198]=-3097.629547
sigma_a=0.220093
R:
[[24.73493422 0.53855308]
[ 0.53855308 25.88175028]]
m0:
[ 3.55552505 -1.80940634 0.12136607 -0.32095902 0.70995223 -0.68009801]
V0:
[[ 2.14863986e-06 -2.18155975e-11 -1.03788158e-12 1.97841308e-11
4.54910298e-14 2.00465211e-14]
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9.87774122e-16 5.22597327e-16]
[-1.03788158e-12 -3.83914089e-14 3.54065483e-09 1.30067253e-14
4.97261045e-17 3.00300865e-17]
[ 1.97841308e-11 3.10194138e-13 1.30067253e-14 2.78109293e-06
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[ 4.54910298e-14 9.87774122e-16 4.97261045e-17 -5.24412287e-12
6.74803833e-09 -1.43444833e-14]
[ 2.00465211e-14 5.22597327e-16 3.00300865e-17 -2.62886281e-12
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LogLike[0199]=-3097.584653
sigma_a=0.219507
R:
[[24.73495197 0.53827862]
[ 0.53827862 25.88217726]]
m0:
[ 3.55552469 -1.80940635 0.12136607 -0.32095883 0.70995224 -0.68009801]
V0:
[[ 2.14863515e-06 -2.19179539e-11 -1.04431999e-12 1.98894732e-11
4.57598008e-14 2.01781086e-14]
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9.94235772e-16 5.26375806e-16]
[-1.04431999e-12 -3.86710403e-14 3.54065479e-09 1.30921076e-14
5.00856407e-17 3.02687642e-17]
[ 1.98894732e-11 3.12026870e-13 1.30921076e-14 2.78108532e-06
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[ 4.57598008e-14 9.94235772e-16 5.00856407e-17 -5.26853157e-12
6.74803821e-09 -1.44488730e-14]
[ 2.01781086e-14 5.26375806e-16 3.02687642e-17 -2.64512649e-12
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LogLike[0200]=-3097.535696
sigma_a=0.218869
R:
[[24.73496922 0.53800399]
[ 0.53800399 25.88260752]]
m0:
[ 3.55552434 -1.80940636 0.12136607 -0.32095864 0.70995224 -0.68009801]
V0:
[[ 2.14863043e-06 -2.20204953e-11 -1.05077599e-12 1.99948571e-11
4.60289166e-14 2.03099884e-14]
[-2.20204953e-11 3.55429751e-08 -3.89516849e-14 3.13861955e-13
1.00071160e-15 5.30166109e-16]
[-1.05077599e-12 -3.89516849e-14 3.54065476e-09 1.31776796e-14
5.04463021e-17 3.05084104e-17]
[ 1.99948571e-11 3.13861955e-13 1.31776796e-14 2.78107770e-06
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[ 4.60289166e-14 1.00071160e-15 5.04463021e-17 -5.29298409e-12
6.74803809e-09 -1.45536403e-14]
[ 2.03099884e-14 5.30166109e-16 3.05084104e-17 -2.66143444e-12
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LogLike[0201]=-3097.487777
sigma_a=0.218244
R:
[[24.73498743 0.5377047 ]
[ 0.5377047 25.88307989]]
m0:
[ 3.55552399 -1.80940637 0.12136607 -0.32095845 0.70995224 -0.68009801]
V0:
[[ 2.14862570e-06 -2.21232391e-11 -1.05725126e-12 2.01002913e-11
4.62984232e-14 2.04421950e-14]
[-2.21232391e-11 3.55429718e-08 -3.92334413e-14 3.15699704e-13
1.00720328e-15 5.33969562e-16]
[-1.05725126e-12 -3.92334413e-14 3.54065472e-09 1.32634638e-14
5.08082149e-17 3.07491314e-17]
[ 2.01002913e-11 3.15699704e-13 1.32634638e-14 2.78107008e-06
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[ 4.62984232e-14 1.00720328e-15 5.08082149e-17 -5.31748461e-12
6.74803798e-09 -1.46588221e-14]
[ 2.04421950e-14 5.33969562e-16 3.07491314e-17 -2.67779095e-12
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LogLike[0202]=-3097.439543
sigma_a=0.217616
R:
[[24.73500486 0.53741034]
[ 0.53741034 25.88354808]]
m0:
[ 3.55552363 -1.80940638 0.12136607 -0.32095826 0.70995224 -0.68009801]
V0:
[[ 2.14862097e-06 -2.22261820e-11 -1.06374551e-12 2.02057690e-11
4.65682981e-14 2.05747155e-14]
[-2.22261820e-11 3.55429684e-08 -3.95162927e-14 3.17539966e-13
1.01371017e-15 5.37785742e-16]
[-1.06374551e-12 -3.95162927e-14 3.54065468e-09 1.33494516e-14
5.11713387e-17 3.09908976e-17]
[ 2.02057690e-11 3.17539966e-13 1.33494516e-14 2.78106245e-06
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[ 4.65682981e-14 1.01371016e-15 5.11713387e-17 -5.34203230e-12
6.74803786e-09 -1.47644119e-14]
[ 2.05747155e-14 5.37785742e-16 3.09908976e-17 -2.69419520e-12
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LogLike[0203]=-3097.393714
sigma_a=0.217020
R:
[[24.73502188 0.5371137 ]
[ 0.5371137 25.88402347]]
m0:
[ 3.55552328 -1.80940639 0.12136607 -0.32095807 0.70995224 -0.68009801]
V0:
[[ 2.14861624e-06 -2.23293262e-11 -1.07025895e-12 2.03112920e-11
4.68385490e-14 2.07075551e-14]
[-2.23293262e-11 3.55429651e-08 -3.98002533e-14 3.19382791e-13
1.02023253e-15 5.41614861e-16]
[-1.07025895e-12 -3.98002533e-14 3.54065465e-09 1.34356466e-14
5.15356939e-17 3.12337265e-17]
[ 2.03112920e-11 3.19382791e-13 1.34356466e-14 2.78105481e-06
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[ 4.68385490e-14 1.02023253e-15 5.15356939e-17 -5.36662766e-12
6.74803774e-09 -1.48704152e-14]
[ 2.07075551e-14 5.41614861e-16 3.12337265e-17 -2.71064778e-12
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LogLike[0204]=-3097.345876
sigma_a=0.216397
R:
[[24.73503766 0.53683086]
[ 0.53683086 25.88448014]]
m0:
[ 3.55552293 -1.8094064 0.12136607 -0.32095788 0.70995224 -0.68009801]
V0:
[[ 2.14861150e-06 -2.24326623e-11 -1.07679072e-12 2.04168548e-11
4.71091497e-14 2.08406952e-14]
[-2.24326623e-11 3.55429618e-08 -4.00852741e-14 3.21228003e-13
1.02676946e-15 5.45456219e-16]
[-1.07679072e-12 -4.00852741e-14 3.54065461e-09 1.35220366e-14
5.19012137e-17 3.14775640e-17]
[ 2.04168548e-11 3.21228003e-13 1.35220366e-14 2.78104716e-06
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[ 4.71091497e-14 1.02676946e-15 5.19012137e-17 -5.39126849e-12
6.74803763e-09 -1.49768135e-14]
[ 2.08406952e-14 5.45456219e-16 3.14775640e-17 -2.72714650e-12
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LogLike[0205]=-3097.302933
sigma_a=0.215839
R:
[[24.73505356 0.53653586]
[ 0.53653586 25.88495993]]
m0:
[ 3.55552258 -1.80940641 0.12136607 -0.32095769 0.70995225 -0.68009801]
V0:
[[ 2.14860676e-06 -2.25361997e-11 -1.08334172e-12 2.05224648e-11
4.73801312e-14 2.09741573e-14]
[-2.25361997e-11 3.55429584e-08 -4.03714086e-14 3.23075811e-13
1.03332201e-15 5.49310628e-16]
[-1.08334172e-12 -4.03714086e-14 3.54065457e-09 1.36086356e-14
5.22679753e-17 3.17224740e-17]
[ 2.05224648e-11 3.23075811e-13 1.36086356e-14 2.78103951e-06
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[ 4.73801312e-14 1.03332201e-15 5.22679753e-17 -5.41595701e-12
6.74803751e-09 -1.50836269e-14]
[ 2.09741573e-14 5.49310628e-16 3.17224740e-17 -2.74369365e-12
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LogLike[0206]=-3097.255936
sigma_a=0.215228
R:
[[24.73506756 0.5362694 ]
[ 0.5362694 25.88539646]]
m0:
[ 3.55552223 -1.80940642 0.12136607 -0.3209575 0.70995225 -0.68009801]
V0:
[[ 2.14860201e-06 -2.26399185e-11 -1.08991007e-12 2.06281089e-11
4.76514342e-14 2.11078993e-14]
[-2.26399185e-11 3.55429551e-08 -4.06585485e-14 3.24925813e-13
1.03988812e-15 5.53176511e-16]
[-1.08991007e-12 -4.06585485e-14 3.54065453e-09 1.36954163e-14
5.26358290e-17 3.19683337e-17]
[ 2.06281089e-11 3.24925813e-13 1.36954163e-14 2.78103185e-06
-5.44068846e-12 -2.76028446e-12]
[ 4.76514342e-14 1.03988812e-15 5.26358290e-17 -5.44068846e-12
6.74803739e-09 -1.51908149e-14]
[ 2.11078993e-14 5.53176511e-16 3.19683337e-17 -2.76028446e-12
-1.51908149e-14 7.06461994e-09]]
LogLike[0207]=-3097.210828
sigma_a=0.214642
R:
[[24.73508227 0.53597889]
[ 0.53597889 25.88587567]]
m0:
[ 3.55552188 -1.80940644 0.12136607 -0.32095731 0.70995225 -0.68009801]
V0:
[[ 2.14859726e-06 -2.27438367e-11 -1.09649752e-12 2.07338021e-11
4.79231203e-14 2.12419637e-14]
[-2.27438367e-11 3.55429517e-08 -4.09467955e-14 3.26778427e-13
1.04646987e-15 5.57055444e-16]
[-1.09649752e-12 -4.09467955e-14 3.54065450e-09 1.37824062e-14
5.30049245e-17 3.22152663e-17]
[ 2.07338021e-11 3.26778427e-13 1.37824062e-14 2.78102418e-06
-5.46546716e-12 -2.77692332e-12]
[ 4.79231203e-14 1.04646987e-15 5.30049245e-17 -5.46546716e-12
6.74803727e-09 -1.52984156e-14]
[ 2.12419637e-14 5.57055444e-16 3.22152663e-17 -2.77692332e-12
-1.52984156e-14 7.06461979e-09]]
LogLike[0208]=-3097.164709
sigma_a=0.214043
R:
[[24.73509609 0.53569855]
[ 0.53569855 25.8863415 ]]
m0:
[ 3.55552154 -1.80940645 0.12136607 -0.32095712 0.70995225 -0.680098 ]
V0:
[[ 2.14859250e-06 -2.28479469e-11 -1.10310337e-12 2.08395355e-11
4.81951575e-14 2.13763301e-14]
[-2.28479469e-11 3.55429483e-08 -4.12361115e-14 3.28633435e-13
1.05306626e-15 5.60946717e-16]
[-1.10310337e-12 -4.12361115e-14 3.54065446e-09 1.38695921e-14
5.33751942e-17 3.24632191e-17]
[ 2.08395355e-11 3.28633435e-13 1.38695921e-14 2.78101650e-06
-5.49029134e-12 -2.79360853e-12]
[ 4.81951575e-14 1.05306626e-15 5.33751942e-17 -5.49029134e-12
6.74803715e-09 -1.54064146e-14]
[ 2.13763301e-14 5.60946717e-16 3.24632191e-17 -2.79360853e-12
-1.54064146e-14 7.06461964e-09]]
LogLike[0209]=-3097.120034
sigma_a=0.213463
R:
[[24.73510974 0.53541188]
[ 0.53541188 25.88682121]]
m0:
[ 3.55552119 -1.80940646 0.12136607 -0.32095693 0.70995225 -0.680098 ]
V0:
[[ 2.14858774e-06 -2.29522545e-11 -1.10972815e-12 2.09453136e-11
4.84675638e-14 2.15110111e-14]
[-2.29522545e-11 3.55429449e-08 -4.15265272e-14 3.30490962e-13
1.05967790e-15 5.64850804e-16]
[-1.10972815e-12 -4.15265272e-14 3.54065442e-09 1.39569823e-14
5.37466833e-17 3.27122299e-17]
[ 2.09453136e-11 3.30490962e-13 1.39569823e-14 2.78100882e-06
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[ 4.84675638e-14 1.05967790e-15 5.37466833e-17 -5.51516225e-12
6.74803703e-09 -1.55148233e-14]
[ 2.15110111e-14 5.64850804e-16 3.27122299e-17 -2.81034138e-12
-1.55148233e-14 7.06461949e-09]]
LogLike[0210]=-3097.075592
sigma_a=0.212886
R:
[[24.73512262 0.53513325]
[ 0.53513325 25.8872908 ]]
m0:
[ 3.55552085 -1.80940647 0.12136607 -0.32095674 0.70995225 -0.680098 ]
V0:
[[ 2.14858297e-06 -2.30567539e-11 -1.11637135e-12 2.10511322e-11
4.87403214e-14 2.16459944e-14]
[-2.30567539e-11 3.55429415e-08 -4.18180145e-14 3.32350883e-13
1.06630420e-15 5.68767263e-16]
[-1.11637135e-12 -4.18180145e-14 3.54065438e-09 1.40445686e-14
5.41193498e-17 3.29622653e-17]
[ 2.10511322e-11 3.32350883e-13 1.40445686e-14 2.78100113e-06
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[ 4.87403214e-14 1.06630420e-15 5.41193498e-17 -5.54007859e-12
6.74803691e-09 -1.56236314e-14]
[ 2.16459944e-14 5.68767263e-16 3.29622653e-17 -2.82712059e-12
-1.56236314e-14 7.06461934e-09]]
LogLike[0211]=-3097.029164
sigma_a=0.212284
R:
[[24.73513502 0.53485576]
[ 0.53485576 25.88776172]]
m0:
[ 3.5555205 -1.80940648 0.12136607 -0.32095655 0.70995226 -0.680098 ]
V0:
[[ 2.14857820e-06 -2.31614450e-11 -1.12303298e-12 2.11569925e-11
4.90134331e-14 2.17812816e-14]
[-2.31614450e-11 3.55429381e-08 -4.21105741e-14 3.34213221e-13
1.07294523e-15 5.72696148e-16]
[-1.12303298e-12 -4.21105741e-14 3.54065434e-09 1.41323522e-14
5.44931987e-17 3.32133298e-17]
[ 2.11569925e-11 3.34213221e-13 1.41323522e-14 2.78099344e-06
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[ 4.90134331e-14 1.07294523e-15 5.44931987e-17 -5.56504031e-12
6.74803679e-09 -1.57328391e-14]
[ 2.17812816e-14 5.72696148e-16 3.32133298e-17 -2.84394616e-12
-1.57328391e-14 7.06461918e-09]]
LogLike[0212]=-3096.987115
sigma_a=0.211738
R:
[[24.73514754 0.53456572]
[ 0.53456572 25.8882574 ]]
m0:
[ 3.55552016 -1.80940649 0.12136607 -0.32095636 0.70995226 -0.680098 ]
V0:
[[ 2.14857343e-06 -2.32663371e-11 -1.12971394e-12 2.12628995e-11
4.92869239e-14 2.19168916e-14]
[-2.32663371e-11 3.55429347e-08 -4.24042609e-14 3.36078143e-13
1.07960191e-15 5.76638195e-16]
[-1.12971394e-12 -4.24042609e-14 3.54065430e-09 1.42203453e-14
5.48683001e-17 3.34654833e-17]
[ 2.12628995e-11 3.36078143e-13 1.42203453e-14 2.78098573e-06
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[ 4.92869239e-14 1.07960191e-15 5.48683001e-17 -5.59004965e-12
6.74803667e-09 -1.58424669e-14]
[ 2.19168916e-14 5.76638195e-16 3.34654833e-17 -2.86082042e-12
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LogLike[0213]=-3096.943685
sigma_a=0.211175
R:
[[24.73515865 0.53430145]
[ 0.53430145 25.88871218]]
m0:
[ 3.55551981 -1.8094065 0.12136607 -0.32095617 0.70995226 -0.680098 ]
V0:
[[ 2.14856865e-06 -2.33714121e-11 -1.13641252e-12 2.13688409e-11
4.95607386e-14 2.20527849e-14]
[-2.33714121e-11 3.55429313e-08 -4.26989747e-14 3.37945275e-13
1.08627233e-15 5.80591934e-16]
[-1.13641252e-12 -4.26989747e-14 3.54065426e-09 1.43085223e-14
5.52445141e-17 3.37186106e-17]
[ 2.13688409e-11 3.37945275e-13 1.43085223e-14 2.78097802e-06
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[ 4.95607386e-14 1.08627233e-15 5.52445141e-17 -5.61510226e-12
6.74803655e-09 -1.59524772e-14]
[ 2.20527849e-14 5.80591934e-16 3.37186106e-17 -2.87773898e-12
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LogLike[0214]=-3096.897836
sigma_a=0.210581
R:
[[24.73516968 0.53402903]
[ 0.53402903 25.88918406]]
m0:
[ 3.55551947 -1.80940651 0.12136607 -0.32095597 0.70995226 -0.680098 ]
V0:
[[ 2.14856387e-06 -2.34766767e-11 -1.14312936e-12 2.14748253e-11
4.98349080e-14 2.21889817e-14]
[-2.34766767e-11 3.55429279e-08 -4.29947551e-14 3.39814826e-13
1.09295746e-15 5.84558085e-16]
[-1.14312936e-12 -4.29947551e-14 3.54065422e-09 1.43968963e-14
5.56219093e-17 3.39727673e-17]
[ 2.14748253e-11 3.39814826e-13 1.43968963e-14 2.78097031e-06
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[ 4.98349080e-14 1.09295746e-15 5.56219093e-17 -5.64019975e-12
6.74803643e-09 -1.60628850e-14]
[ 2.21889817e-14 5.84558085e-16 3.39727673e-17 -2.89470353e-12
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LogLike[0215]=-3096.851521
sigma_a=0.209982
R:
[[24.73518092 0.53374107]
[ 0.53374107 25.88968628]]
m0:
[ 3.55551913 -1.80940652 0.12136607 -0.32095578 0.70995226 -0.680098 ]
V0:
[[ 2.14855908e-06 -2.35821424e-11 -1.14986561e-12 2.15808568e-11
5.01094573e-14 2.23255023e-14]
[-2.35821424e-11 3.55429244e-08 -4.32916687e-14 3.41686967e-13
1.09965828e-15 5.88537469e-16]
[-1.14986561e-12 -4.32916687e-14 3.54065418e-09 1.44854804e-14
5.60005637e-17 3.42280215e-17]
[ 2.15808568e-11 3.41686967e-13 1.44854804e-14 2.78096258e-06
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[ 5.01094573e-14 1.09965828e-15 5.60005637e-17 -5.66534486e-12
6.74803631e-09 -1.61737151e-14]
[ 2.23255023e-14 5.88537469e-16 3.42280215e-17 -2.91171689e-12
-1.61737151e-14 7.06461857e-09]]
LogLike[0216]=-3096.812177
sigma_a=0.209472
R:
[[24.73519195 0.53344927]
[ 0.53344927 25.89019884]]
m0:
[ 3.55551879 -1.80940653 0.12136607 -0.32095559 0.70995226 -0.680098 ]
V0:
[[ 2.14855429e-06 -2.36878120e-11 -1.15662154e-12 2.16869334e-11
5.03843852e-14 2.24623482e-14]
[-2.36878120e-11 3.55429210e-08 -4.35897340e-14 3.43561691e-13
1.10637489e-15 5.92530203e-16]
[-1.15662154e-12 -4.35897340e-14 3.54065415e-09 1.45742757e-14
5.63804886e-17 3.44843858e-17]
[ 2.16869334e-11 3.43561691e-13 1.45742757e-14 2.78095485e-06
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[ 5.03843852e-14 1.10637489e-15 5.63804886e-17 -5.69053826e-12
6.74803619e-09 -1.62849743e-14]
[ 2.24623482e-14 5.92530203e-16 3.44843858e-17 -2.92877981e-12
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LogLike[0217]=-3096.771178
sigma_a=0.208942
R:
[[24.73520116 0.53319958]
[ 0.53319958 25.8906404 ]]
m0:
[ 3.55551845 -1.80940654 0.12136607 -0.3209554 0.70995227 -0.680098 ]
V0:
[[ 2.14854949e-06 -2.37936555e-11 -1.16339428e-12 2.17930386e-11
5.06596105e-14 2.25994592e-14]
[-2.37936555e-11 3.55429175e-08 -4.38887834e-14 3.45438443e-13
1.11310433e-15 5.96533973e-16]
[-1.16339428e-12 -4.38887834e-14 3.54065411e-09 1.46632430e-14
5.67614637e-17 3.47416756e-17]
[ 2.17930386e-11 3.45438443e-13 1.46632430e-14 2.78094711e-06
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[ 5.06596105e-14 1.11310433e-15 5.67614637e-17 -5.71577276e-12
6.74803607e-09 -1.63966000e-14]
[ 2.25994592e-14 5.96533973e-16 3.47416756e-17 -2.94588500e-12
-1.63966000e-14 7.06461825e-09]]
LogLike[0218]=-3096.728619
sigma_a=0.208391
R:
[[24.73521032 0.5329405 ]
[ 0.5329405 25.89110132]]
m0:
[ 3.55551811 -1.80940655 0.12136607 -0.32095521 0.70995227 -0.680098 ]
V0:
[[ 2.14854469e-06 -2.38996809e-11 -1.17018462e-12 2.18991851e-11
5.09351760e-14 2.27368620e-14]
[-2.38996809e-11 3.55429140e-08 -4.41888638e-14 3.47317516e-13
1.11984793e-15 6.00549726e-16]
[-1.17018462e-12 -4.41888638e-14 3.54065407e-09 1.47523998e-14
5.71435789e-17 3.49999628e-17]
[ 2.18991851e-11 3.47317516e-13 1.47523998e-14 2.78093937e-06
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[ 5.09351760e-14 1.11984793e-15 5.71435789e-17 -5.74105033e-12
6.74803594e-09 -1.65086100e-14]
[ 2.27368620e-14 6.00549726e-16 3.49999628e-17 -2.96303447e-12
-1.65086100e-14 7.06461809e-09]]
LogLike[0219]=-3096.682801
sigma_a=0.207799
R:
[[24.73521953 0.53267107]
[ 0.53267107 25.89158372]]
m0:
[ 3.55551778 -1.80940656 0.12136607 -0.32095501 0.70995227 -0.680098 ]
V0:
[[ 2.14853988e-06 -2.40058959e-11 -1.17699332e-12 2.20053746e-11
5.12110963e-14 2.28745694e-14]
[-2.40058959e-11 3.55429106e-08 -4.44900208e-14 3.49199009e-13
1.12660630e-15 6.04577988e-16]
[-1.17699332e-12 -4.44900208e-14 3.54065403e-09 1.48417543e-14
5.75268847e-17 3.52592920e-17]
[ 2.20053746e-11 3.49199009e-13 1.48417543e-14 2.78093161e-06
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[ 5.12110963e-14 1.12660630e-15 5.75268847e-17 -5.76637279e-12
6.74803582e-09 -1.66210210e-14]
[ 2.28745694e-14 6.04577988e-16 3.52592920e-17 -2.98023015e-12
-1.66210210e-14 7.06461793e-09]]
LogLike[0220]=-3096.638403
sigma_a=0.207225
R:
[[24.73522908 0.53238072]
[ 0.53238072 25.89210708]]
m0:
[ 3.55551744 -1.80940657 0.12136607 -0.32095482 0.70995227 -0.680098 ]
V0:
[[ 2.14853507e-06 -2.41123160e-11 -1.18382189e-12 2.21116128e-11
5.14874057e-14 2.30126090e-14]
[-2.41123160e-11 3.55429071e-08 -4.47923438e-14 3.51083155e-13
1.13338077e-15 6.08619873e-16]
[-1.18382189e-12 -4.47923438e-14 3.54065399e-09 1.49313243e-14
5.79114868e-17 3.55197555e-17]
[ 2.21116128e-11 3.51083155e-13 1.49313243e-14 2.78092385e-06
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[ 5.14874057e-14 1.13338077e-15 5.79114868e-17 -5.79174380e-12
6.74803570e-09 -1.67338666e-14]
[ 2.30126090e-14 6.08619873e-16 3.55197555e-17 -2.99747583e-12
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LogLike[0221]=-3096.596484
sigma_a=0.206683
R:
[[24.73523809 0.53209798]
[ 0.53209798 25.89262028]]
m0:
[ 3.5555171 -1.80940658 0.12136607 -0.32095463 0.70995227 -0.680098 ]
V0:
[[ 2.14853026e-06 -2.42189356e-11 -1.19066982e-12 2.22178923e-11
5.17640780e-14 2.31509643e-14]
[-2.42189356e-11 3.55429036e-08 -4.50958039e-14 3.52969776e-13
1.14017055e-15 6.12674810e-16]
[-1.19066982e-12 -4.50958039e-14 3.54065394e-09 1.50210993e-14
5.82973309e-17 3.57813111e-17]
[ 2.22178923e-11 3.52969776e-13 1.50210993e-14 2.78091609e-06
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[ 5.17640780e-14 1.14017055e-15 5.82973309e-17 -5.81716202e-12
6.74803557e-09 -1.68471357e-14]
[ 2.31509643e-14 6.12674810e-16 3.57813111e-17 -3.01477020e-12
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LogLike[0222]=-3096.551226
sigma_a=0.206098
R:
[[24.73524635 0.53183026]
[ 0.53183026 25.89310941]]
m0:
[ 3.55551677 -1.80940659 0.12136607 -0.32095443 0.70995227 -0.68009799]
V0:
[[ 2.14852544e-06 -2.43257444e-11 -1.19753611e-12 2.23242091e-11
5.20410895e-14 2.32896167e-14]
[-2.43257444e-11 3.55429001e-08 -4.54003439e-14 3.54858711e-13
1.14697473e-15 6.16742070e-16]
[-1.19753611e-12 -4.54003439e-14 3.54065390e-09 1.51110671e-14
5.86843477e-17 3.60439002e-17]
[ 2.23242091e-11 3.54858711e-13 1.51110671e-14 2.78090831e-06
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[ 5.20410895e-14 1.14697473e-15 5.86843477e-17 -5.84262498e-12
6.74803545e-09 -1.69608072e-14]
[ 2.32896167e-14 6.16742070e-16 3.60439002e-17 -3.03211077e-12
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LogLike[0223]=-3096.508674
sigma_a=0.205549
R:
[[24.73525485 0.53154173]
[ 0.53154173 25.89364003]]
m0:
[ 3.55551643 -1.8094066 0.12136607 -0.32095424 0.70995228 -0.68009799]
V0:
[[ 2.14852062e-06 -2.44327583e-11 -1.20442233e-12 2.24305743e-11
5.23184894e-14 2.34286018e-14]
[-2.44327582e-11 3.55428965e-08 -4.57060569e-14 3.56750295e-13
1.15379503e-15 6.20823012e-16]
[-1.20442233e-12 -4.57060569e-14 3.54065386e-09 1.52012509e-14
5.90726665e-17 3.63076317e-17]
[ 2.24305743e-11 3.56750295e-13 1.52012509e-14 2.78090053e-06
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[ 5.23184894e-14 1.15379503e-15 5.90726665e-17 -5.86813650e-12
6.74803533e-09 -1.70749157e-14]
[ 2.34286018e-14 6.20823012e-16 3.63076317e-17 -3.04950151e-12
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LogLike[0224]=-3096.465105
sigma_a=0.204986
R:
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LogLike[0225]=-3096.423345
sigma_a=0.204447
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LogLike[0226]=-3096.381713
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m0:
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LogLike[0227]=-3096.338930
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LogLike[0228]=-3096.296326
sigma_a=0.202809
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LogLike[0229]=-3096.253783
sigma_a=0.202261
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LogLike[0230]=-3096.211144
sigma_a=0.201711
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LogLike[0231]=-3096.169011
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LogLike[0232]=-3096.130868
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LogLike[0233]=-3096.089019
sigma_a=0.200138
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m0:
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LogLike[0234]=-3096.044684
sigma_a=0.199567
R:
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m0:
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LogLike[0235]=-3096.004393
sigma_a=0.199049
R:
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m0:
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LogLike[0236]=-3095.963791
sigma_a=0.198526
R:
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m0:
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LogLike[0237]=-3095.924736
sigma_a=0.198024
R:
[[24.73534112 0.52773033]
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m0:
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LogLike[0238]=-3095.888885
sigma_a=0.197563
R:
[[24.73534549 0.52747268]
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m0:
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LogLike[0239]=-3095.845212
sigma_a=0.197001
R:
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m0:
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LogLike[0240]=-3095.808573
sigma_a=0.196530
R:
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m0:
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LogLike[0241]=-3095.769894
sigma_a=0.196033
R:
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m0:
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V0:
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LogLike[0242]=-3095.732294
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LogLike[0252]=-3095.352520
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LogLike[0253]=-3095.315517
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LogLike[0255]=-3095.239778
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LogLike[0256]=-3095.203534
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LogLike[0257]=-3095.167800
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LogLike[0258]=-3095.130211
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LogLike[0259]=-3095.095575
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LogLike[0261]=-3095.026977
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LogLike[0262]=-3094.994038
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LogLike[0263]=-3094.961068
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LogLike[0264]=-3094.926796
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LogLike[0265]=-3094.890096
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LogLike[0266]=-3094.855020
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LogLike[0267]=-3094.822388
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LogLike[0268]=-3094.790388
sigma_a=0.183458
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LogLike[0269]=-3094.757118
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LogLike[0270]=-3094.724155
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LogLike[0271]=-3094.689703
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LogLike[0272]=-3094.656694
sigma_a=0.181743
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m0:
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LogLike[0273]=-3094.625354
sigma_a=0.181340
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LogLike[0274]=-3094.594406
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R:
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m0:
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LogLike[0275]=-3094.563069
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LogLike[0276]=-3094.531803
sigma_a=0.180140
R:
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LogLike[0277]=-3094.500608
sigma_a=0.179739
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LogLike[0278]=-3094.470707
sigma_a=0.179355
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LogLike[0279]=-3094.436245
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LogLike[0280]=-3094.403439
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LogLike[0281]=-3094.373578
sigma_a=0.178108
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m0:
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LogLike[0282]=-3094.343159
sigma_a=0.177717
R:
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m0:
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LogLike[0283]=-3094.309791
sigma_a=0.177289
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m0:
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LogLike[0284]=-3094.276533
sigma_a=0.176861
R:
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m0:
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LogLike[0285]=-3094.246905
sigma_a=0.176480
R:
[[24.7354973 0.51604433]
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m0:
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LogLike[0286]=-3094.216604
sigma_a=0.176091
R:
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m0:
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LogLike[0287]=-3094.182257
sigma_a=0.175650
R:
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m0:
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LogLike[0288]=-3094.145728
sigma_a=0.175180
R:
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m0:
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LogLike[0289]=-3094.117136
sigma_a=0.174812
R:
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m0:
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LogLike[0290]=-3094.084835
sigma_a=0.174397
R:
[[24.73552268 0.51486765]
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m0:
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LogLike[0291]=-3094.056044
sigma_a=0.174026
R:
[[24.73552835 0.5146283 ]
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m0:
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[[ 2.14818258e-06 -3.21571558e-11 -1.71707261e-12 2.97540831e-11
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LogLike[0292]=-3094.031158
sigma_a=0.173706
R:
[[24.7355337 0.51441304]
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m0:
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LogLike[0293]=-3094.003522
sigma_a=0.173350
R:
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m0:
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V0:
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LogLike[0294]=-3093.977447
sigma_a=0.173015
R:
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m0:
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V0:
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LogLike[0295]=-3093.948632
sigma_a=0.172644
R:
[[24.73554914 0.51382415]
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m0:
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V0:
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LogLike[0297]=-3093.890539
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m0:
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LogLike[0298]=-3093.864504
sigma_a=0.171560
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LogLike[0299]=-3093.835818
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m0:
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LogLike[0300]=-3093.807128
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LogLike[0301]=-3093.775539
sigma_a=0.170414
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LogLike[0302]=-3093.748114
sigma_a=0.170060
R:
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m0:
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LogLike[0303]=-3093.718433
sigma_a=0.169677
R:
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m0:
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LogLike[0304]=-3093.691742
sigma_a=0.169333
R:
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m0:
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LogLike[0305]=-3093.662706
sigma_a=0.168958
R:
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m0:
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LogLike[0306]=-3093.632724
sigma_a=0.168571
R:
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m0:
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LogLike[0307]=-3093.603488
sigma_a=0.168193
R:
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m0:
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LogLike[0308]=-3093.579158
sigma_a=0.167879
R:
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m0:
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LogLike[0309]=-3093.552522
sigma_a=0.167535
R:
[[24.73565273 0.51078011]
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m0:
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LogLike[0310]=-3093.522617
sigma_a=0.167148
R:
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m0:
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V0:
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LogLike[0311]=-3093.497423
sigma_a=0.166822
R:
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m0:
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V0:
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LogLike[0312]=-3093.469529
sigma_a=0.166462
R:
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m0:
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V0:
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LogLike[0313]=-3093.439390
sigma_a=0.166072
R:
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LogLike[0321]=-3093.224975
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LogLike[0323]=-3093.166472
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LogLike[0324]=-3093.142718
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LogLike[0325]=-3093.115629
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LogLike[0326]=-3093.088351
sigma_a=0.161517
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LogLike[0327]=-3093.063123
sigma_a=0.161189
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LogLike[0328]=-3093.039674
sigma_a=0.160883
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LogLike[0329]=-3093.014100
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LogLike[0330]=-3092.989935
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LogLike[0331]=-3092.961437
sigma_a=0.159864
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[[24.73591741 0.50605682]
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8.42483011e-14 4.02100300e-14]
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1.97622714e-15 1.13725150e-15]
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LogLike[0332]=-3092.935581
sigma_a=0.159527
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[[24.73593457 0.50582175]
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m0:
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[[ 2.14796992e-06 -3.72202216e-11 -2.06764196e-12 3.42457252e-11
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1.98461442e-15 1.14273577e-15]
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1.08735508e-16 7.17284519e-17]
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6.74801981e-09 -3.20586869e-14]
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LogLike[0333]=-3092.909888
sigma_a=0.159191
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[[24.73595076 0.50560629]
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m0:
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1.99301563e-15 1.14823319e-15]
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1.09258624e-16 7.21167408e-17]
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LogLike[0334]=-3092.885942
sigma_a=0.158879
R:
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m0:
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2.00143107e-15 1.15374397e-15]
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1.09783011e-16 7.25062568e-17]
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LogLike[0335]=-3092.867492
sigma_a=0.158637
R:
[[24.73598293 0.50519149]
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m0:
[ 3.55548451 -1.80940753 0.12136609 -0.32093129 0.70995246 -0.6800979 ]
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[[ 2.14795411e-06 -3.76025208e-11 -2.09453807e-12 3.45764090e-11
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6.74801933e-09 -3.25441086e-14]
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LogLike[0336]=-3092.842871
sigma_a=0.158316
R:
[[24.73599544 0.50503553]
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m0:
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LogLike[0337]=-3092.822587
sigma_a=0.158050
R:
[[24.73601205 0.50483013]
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m0:
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LogLike[0338]=-3092.791797
sigma_a=0.157648
R:
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m0:
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LogLike[0339]=-3092.768565
sigma_a=0.157344
R:
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m0:
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LogLike[0340]=-3092.748587
sigma_a=0.157082
R:
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m0:
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LogLike[0341]=-3092.725023
sigma_a=0.156773
R:
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m0:
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LogLike[0342]=-3092.698563
sigma_a=0.156426
R:
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m0:
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LogLike[0343]=-3092.676651
sigma_a=0.156139
R:
[[24.73611882 0.50360574]
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m0:
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LogLike[0344]=-3092.654043
sigma_a=0.155842
R:
[[24.73613652 0.50341673]
[ 0.50341673 25.96306863]]
m0:
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LogLike[0345]=-3092.628122
sigma_a=0.155502
R:
[[24.73615497 0.50322244]
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m0:
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LogLike[0346]=-3092.598531
sigma_a=0.155113
R:
[[24.73617652 0.50299967]
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m0:
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V0:
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LogLike[0347]=-3092.571566
sigma_a=0.154758
R:
[[24.73620176 0.50274443]
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m0:
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LogLike[0348]=-3092.549009
sigma_a=0.154461
R:
[[24.73622559 0.5025095 ]
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m0:
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V0:
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LogLike[0355]=-3092.398894
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LogLike[0356]=-3092.376891
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LogLike[0357]=-3092.351722
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LogLike[0358]=-3092.332207
sigma_a=0.151599
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LogLike[0359]=-3092.309602
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LogLike[0360]=-3092.287683
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LogLike[0361]=-3092.266062
sigma_a=0.150722
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LogLike[0362]=-3092.244496
sigma_a=0.150436
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m0:
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LogLike[0363]=-3092.228000
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m0:
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LogLike[0364]=-3092.203391
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R:
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m0:
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LogLike[0365]=-3092.178077
sigma_a=0.149553
R:
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LogLike[0366]=-3092.159643
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R:
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m0:
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2.27781805e-15 1.33680552e-15]
[-2.38212216e-12 -1.01244081e-13 3.54064597e-09 3.00893533e-14
1.27202595e-16 8.55932628e-17]
[ 3.80078253e-11 6.50298234e-13 3.00893533e-14 2.77972175e-06
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[ 9.53663132e-14 2.27781805e-15 1.27202595e-16 -9.96633540e-12
6.74801417e-09 -3.77925224e-14]
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LogLike[0367]=-3092.141328
sigma_a=0.149063
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[[24.73667056 0.49881778]
[ 0.49881778 25.97743978]]
m0:
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[[ 2.14778347e-06 -4.17761954e-11 -2.39169370e-12 3.81189311e-11
9.56890334e-14 4.65529740e-14]
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2.28667611e-15 1.34273733e-15]
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1.27767050e-16 8.60219621e-17]
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[ 9.56890334e-14 2.28667611e-15 1.27767050e-16 -9.99798771e-12
6.74801400e-09 -3.79689687e-14]
[ 4.65529740e-14 1.34273733e-15 8.60219621e-17 -6.04521229e-12
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LogLike[0368]=-3092.119049
sigma_a=0.148766
R:
[[24.73669435 0.49864846]
[ 0.49864846 25.97798111]]
m0:
[ 3.55547708 -1.80940772 0.1213661 -0.32092416 0.70995252 -0.68009787]
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[[ 2.14777808e-06 -4.19094140e-11 -2.40128120e-12 3.82300580e-11
9.60119917e-14 4.67342427e-14]
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2.29554568e-15 1.34868022e-15]
[-2.40128120e-12 -1.02191639e-13 3.54064583e-09 3.03244643e-14
1.28332560e-16 8.64517065e-17]
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[ 9.60119917e-14 2.29554568e-15 1.28332560e-16 -1.00296745e-11
6.74801383e-09 -3.81458049e-14]
[ 4.67342427e-14 1.34868022e-15 8.64517065e-17 -6.06938315e-12
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LogLike[0369]=-3092.100074
sigma_a=0.148513
R:
[[24.7367236 0.498443 ]
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m0:
[ 3.55547687 -1.80940773 0.1213661 -0.32092394 0.70995252 -0.68009787]
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[[ 2.14777269e-06 -4.20428120e-11 -2.41088822e-12 3.83412206e-11
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2.30443005e-15 1.35463717e-15]
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1.28899408e-16 8.68827656e-17]
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[ 9.63352688e-14 2.30443005e-15 1.28899408e-16 -1.00614035e-11
6.74801366e-09 -3.83231181e-14]
[ 4.69158169e-14 1.35463717e-15 8.68827656e-17 -6.09360296e-12
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LogLike[0370]=-3092.078807
sigma_a=0.148229
R:
[[24.73674928 0.49826569]
[ 0.49826569 25.97920774]]
m0:
[ 3.55547665 -1.80940773 0.1213661 -0.32092372 0.70995252 -0.68009787]
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[[ 2.14776729e-06 -4.21763633e-11 -2.42051193e-12 3.84523989e-11
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2.31332593e-15 1.36060539e-15]
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1.29467328e-16 8.73148949e-17]
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[ 9.66587759e-14 2.31332593e-15 1.29467328e-16 -1.00931684e-11
6.74801348e-09 -3.85008391e-14]
[ 4.70976285e-14 1.36060539e-15 8.73148949e-17 -6.11786454e-12
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LogLike[0371]=-3092.064193
sigma_a=0.148034
R:
[[24.73677829 0.49806789]
[ 0.49806789 25.9798432 ]]
m0:
[ 3.55547644 -1.80940774 0.1213661 -0.3209235 0.70995253 -0.68009787]
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[[ 2.14776189e-06 -4.23100871e-11 -2.43015443e-12 3.85636077e-11
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2.32223577e-15 1.36658696e-15]
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1.30036519e-16 8.77482788e-17]
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[ 9.69825790e-14 2.32223577e-15 1.30036519e-16 -1.01249739e-11
6.74801331e-09 -3.86790200e-14]
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LogLike[0372]=-3092.042657
sigma_a=0.147746
R:
[[24.73679893 0.4979294 ]
[ 0.4979294 25.98028876]]
m0:
[ 3.55547623 -1.80940774 0.1213661 -0.32092329 0.70995253 -0.68009787]
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[[ 2.14775649e-06 -4.24439297e-11 -2.43980989e-12 3.86748192e-11
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2.33115379e-15 1.37257678e-15]
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1.30606495e-16 8.81824591e-17]
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6.74801314e-09 -3.88575176e-14]
[ 4.74619966e-14 1.37257678e-15 8.81824591e-17 -6.16651425e-12
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LogLike[0373]=-3092.023661
sigma_a=0.147492
R:
[[24.736829 0.49772917]
[ 0.49772917 25.98093383]]
m0:
[ 3.55547602 -1.80940775 0.1213661 -0.32092307 0.70995253 -0.68009787]
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[[ 2.14775108e-06 -4.25779482e-11 -2.44948453e-12 3.87860717e-11
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2.34008684e-15 1.37858077e-15]
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LogLike[0374]=-3092.003882
sigma_a=0.147227
R:
[[24.73685642 0.49754974]
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m0:
[ 3.55547581 -1.80940775 0.1213661 -0.32092285 0.70995253 -0.68009787]
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LogLike[0375]=-3091.977741
sigma_a=0.146876
R:
[[24.73688528 0.49736319]
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m0:
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V0:
[[ 2.14774026e-06 -4.28464593e-11 -2.46888556e-12 3.90086374e-11
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6.74801262e-09 -3.93956887e-14]
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LogLike[0376]=-3091.962785
sigma_a=0.146676
R:
[[24.73692389 0.49711719]
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m0:
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V0:
[[ 2.14773484e-06 -4.29810125e-11 -2.47861859e-12 3.91199793e-11
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2.36696549e-15 1.39666960e-15]
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[ 4.81939505e-14 1.39666960e-15 8.99317295e-17 -6.26434681e-12
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LogLike[0377]=-3091.942869
sigma_a=0.146409
R:
[[24.73694715 0.49697196]
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m0:
[ 3.55547519 -1.80940777 0.1213661 -0.3209222 0.70995254 -0.68009786]
V0:
[[ 2.14772942e-06 -4.31156896e-11 -2.48836516e-12 3.92313142e-11
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[-4.31156896e-11 3.55422415e-08 -1.06512805e-13 6.74604224e-13
2.37594922e-15 1.40272297e-15]
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[ 3.92313142e-11 6.74604224e-13 3.13908919e-14 2.77962615e-06
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6.74801227e-09 -3.97568192e-14]
[ 4.83775747e-14 1.40272297e-15 9.03719208e-17 -6.28891606e-12
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LogLike[0378]=-3091.924634
sigma_a=0.146164
R:
[[24.73697756 0.4967833 ]
[ 0.4967833 25.98399536]]
m0:
[ 3.55547499 -1.80940777 0.1213661 -0.32092198 0.70995254 -0.68009786]
V0:
[[ 2.14772400e-06 -4.32505324e-11 -2.49812987e-12 3.93426837e-11
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[-4.32505324e-11 3.55422365e-08 -1.06998789e-13 6.76824669e-13
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[-2.49812987e-12 -1.06998789e-13 3.54064511e-09 3.15102395e-14
1.34052412e-16 9.08133511e-17]
[ 3.93426837e-11 6.76824669e-13 3.15102395e-14 2.77961743e-06
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[ 9.92562432e-14 2.38494683e-15 1.34052412e-16 -1.03486401e-11
6.74801209e-09 -3.99379963e-14]
[ 4.85614851e-14 1.40878955e-15 9.08133511e-17 -6.31353081e-12
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LogLike[0379]=-3091.905936
sigma_a=0.145912
R:
[[24.73700616 0.49660842]
[ 0.49660842 25.9845639 ]]
m0:
[ 3.55547478 -1.80940778 0.1213661 -0.32092176 0.70995254 -0.68009786]
V0:
[[ 2.14771857e-06 -4.33855275e-11 -2.50791126e-12 3.94540716e-11
9.95820929e-14 4.87456366e-14]
[-4.33855275e-11 3.55422315e-08 -1.07485875e-13 6.79046726e-13
2.39395613e-15 1.41486756e-15]
[-2.50791126e-12 -1.07485875e-13 3.54064504e-09 3.16297438e-14
1.34630781e-16 9.12558702e-17]
[ 3.94540716e-11 6.79046727e-13 3.16297438e-14 2.77960870e-06
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[ 9.95820929e-14 2.39395612e-15 1.34630781e-16 -1.03807440e-11
6.74801192e-09 -4.01195831e-14]
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LogLike[0380]=-3091.886420
sigma_a=0.145650
R:
[[24.73703585 0.4964291 ]
[ 0.4964291 25.98514765]]
m0:
[ 3.55547458 -1.80940778 0.1213661 -0.32092154 0.70995254 -0.68009786]
V0:
[[ 2.14771315e-06 -4.35206794e-11 -2.51770981e-12 3.95654830e-11
9.99081989e-14 4.89300439e-14]
[-4.35206794e-11 3.55422266e-08 -1.07974097e-13 6.81270533e-13
2.40297782e-15 1.42095759e-15]
[-2.51770981e-12 -1.07974097e-13 3.54064496e-09 3.17494141e-14
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[ 3.95654830e-11 6.81270533e-13 3.17494141e-14 2.77959997e-06
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6.74801174e-09 -4.03015921e-14]
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LogLike[0381]=-3091.870534
sigma_a=0.145436
R:
[[24.73706732 0.49624144]
[ 0.49624144 25.98575942]]
m0:
[ 3.55547437 -1.80940779 0.1213661 -0.32092133 0.70995254 -0.68009786]
V0:
[[ 2.14770771e-06 -4.36559955e-11 -2.52752636e-12 3.96769200e-11
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[-4.36559955e-11 3.55422216e-08 -1.08463511e-13 6.83496189e-13
2.41201258e-15 1.42706027e-15]
[-2.52752636e-12 -1.08463511e-13 3.54064489e-09 3.18692594e-14
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6.74801156e-09 -4.04840439e-14]
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LogLike[0382]=-3091.848792
sigma_a=0.145143
R:
[[24.73709357 0.49608702]
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m0:
[ 3.55547417 -1.80940779 0.1213661 -0.32092111 0.70995254 -0.68009786]
V0:
[[ 2.14770228e-06 -4.37914455e-11 -2.53735762e-12 3.97883676e-11
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2.42105719e-15 1.43317274e-15]
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[ 1.00561145e-13 2.42105719e-15 1.36372659e-16 -1.04772745e-11
6.74801139e-09 -4.06668576e-14]
[ 4.92996009e-14 1.43317274e-15 9.25901857e-17 -6.41241265e-12
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LogLike[0383]=-3091.831687
sigma_a=0.144912
R:
[[24.73712946 0.495878 ]
[ 0.495878 25.98694661]]
m0:
[ 3.55547397 -1.8094078 0.1213661 -0.32092089 0.70995255 -0.68009786]
V0:
[[ 2.14769684e-06 -4.39270800e-11 -2.54720910e-12 3.98998550e-11
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[-4.39270800e-11 3.55422115e-08 -1.09445628e-13 6.87952415e-13
2.43011737e-15 1.43930003e-15]
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1.36955717e-16 9.30373778e-17]
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6.74801121e-09 -4.08501697e-14]
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LogLike[0384]=-3091.811034
sigma_a=0.144634
R:
[[24.73715868 0.49571047]
[ 0.49571047 25.9874949 ]]
m0:
[ 3.55547376 -1.8094078 0.1213661 -0.32092067 0.70995255 -0.68009786]
V0:
[[ 2.14769140e-06 -4.40628599e-11 -2.55707655e-12 4.00113527e-11
1.01215161e-13 4.96702219e-14]
[-4.40628599e-11 3.55422065e-08 -1.09938416e-13 6.90183068e-13
2.43918815e-15 1.44543789e-15]
[-2.55707655e-12 -1.09938416e-13 3.54064467e-09 3.22297503e-14
1.37539782e-16 9.34855887e-17]
[ 4.00113527e-11 6.90183068e-13 3.22297503e-14 2.77956499e-06
-1.05418166e-11 -6.46211671e-12]
[ 1.01215161e-13 2.43918815e-15 1.37539782e-16 -1.05418166e-11
6.74801104e-09 -4.10338753e-14]
[ 4.96702219e-14 1.44543789e-15 9.34855887e-17 -6.46211671e-12
-4.10338753e-14 7.06458096e-09]]
LogLike[0385]=-3091.792457
sigma_a=0.144383
R:
[[24.73719401 0.49551001]
[ 0.49551001 25.98815184]]
m0:
[ 3.55547356 -1.80940781 0.1213661 -0.32092046 0.70995255 -0.68009786]
V0:
[[ 2.14768595e-06 -4.41988162e-11 -2.56696339e-12 4.01228834e-11
1.01542578e-13 4.98559411e-14]
[-4.41988162e-11 3.55422015e-08 -1.10432490e-13 6.92415820e-13
2.44827343e-15 1.45158969e-15]
[-2.56696339e-12 -1.10432490e-13 3.54064459e-09 3.23502724e-14
1.38125174e-16 9.39351175e-17]
[ 4.01228834e-11 6.92415820e-13 3.23502724e-14 2.77955624e-06
-1.05741462e-11 -6.48703729e-12]
[ 1.01542578e-13 2.44827343e-15 1.38125174e-16 -1.05741462e-11
6.74801086e-09 -4.12180590e-14]
[ 4.98559411e-14 1.45158969e-15 9.39351175e-17 -6.48703729e-12
-4.12180590e-14 7.06458066e-09]]
LogLike[0386]=-3091.781049
sigma_a=0.144229
R:
[[24.73722662 0.49532764]
[ 0.49532764 25.98875028]]
m0:
[ 3.55547336 -1.80940781 0.1213661 -0.32092024 0.70995255 -0.68009786]
V0:
[[ 2.14768050e-06 -4.43349318e-11 -2.57686773e-12 4.02344321e-11
1.01870238e-13 5.00419107e-14]
[-4.43349318e-11 3.55421964e-08 -1.10927727e-13 6.94650229e-13
2.45737085e-15 1.45775345e-15]
[-2.57686773e-12 -1.10927727e-13 3.54064452e-09 3.24709571e-14
1.38711703e-16 9.43857911e-17]
[ 4.02344321e-11 6.94650229e-13 3.24709571e-14 2.77954748e-06
-1.06065130e-11 -6.51200168e-12]
[ 1.01870238e-13 2.45737085e-15 1.38711703e-16 -1.06065130e-11
6.74801068e-09 -4.14026746e-14]
[ 5.00419107e-14 1.45775345e-15 9.43857911e-17 -6.51200168e-12
-4.14026746e-14 7.06458037e-09]]
LogLike[0387]=-3091.761893
sigma_a=0.143970
R:
[[24.73724726 0.49521364]
[ 0.49521364 25.98912476]]
m0:
[ 3.55547316 -1.80940782 0.1213661 -0.32092002 0.70995255 -0.68009786]
V0:
[[ 2.14767505e-06 -4.44711451e-11 -2.58678284e-12 4.03459771e-11
1.02198005e-13 5.02280102e-14]
[-4.44711451e-11 3.55421914e-08 -1.11423679e-13 6.96885364e-13
2.46647458e-15 1.46392377e-15]
[-2.58678284e-12 -1.11423679e-13 3.54064444e-09 3.25917262e-14
1.39298858e-16 9.48371130e-17]
[ 4.03459771e-11 6.96885364e-13 3.25917262e-14 2.77953871e-06
-1.06389027e-11 -6.53699291e-12]
[ 1.02198005e-13 2.46647457e-15 1.39298858e-16 -1.06389027e-11
6.74801050e-09 -4.15875557e-14]
[ 5.02280102e-14 1.46392377e-15 9.48371130e-17 -6.53699291e-12
-4.15875557e-14 7.06458007e-09]]
LogLike[0388]=-3091.743973
sigma_a=0.143727
R:
[[24.73728118 0.49502732]
[ 0.49502732 25.98973747]]
m0:
[ 3.55547296 -1.80940782 0.1213661 -0.3209198 0.70995256 -0.68009785]
V0:
[[ 2.14766960e-06 -4.46075236e-11 -2.59671613e-12 4.04575630e-11
1.02526089e-13 5.04144067e-14]
[-4.46075236e-11 3.55421863e-08 -1.11920841e-13 6.99122663e-13
2.47559271e-15 1.47010774e-15]
[-2.59671613e-12 -1.11920841e-13 3.54064437e-09 3.27126881e-14
1.39887310e-16 9.52897139e-17]
[ 4.04575630e-11 6.99122663e-13 3.27126881e-14 2.77952994e-06
-1.06713312e-11 -6.56202978e-12]
[ 1.02526089e-13 2.47559271e-15 1.39887310e-16 -1.06713313e-11
6.74801032e-09 -4.17728864e-14]
[ 5.04144067e-14 1.47010774e-15 9.52897139e-17 -6.56202978e-12
-4.17728864e-14 7.06457978e-09]]
LogLike[0389]=-3091.724302
sigma_a=0.143460
R:
[[24.73731389 0.49485012]
[ 0.49485012 25.99032087]]
m0:
[ 3.55547277 -1.80940783 0.1213661 -0.32091958 0.70995256 -0.68009785]
V0:
[[ 2.14766414e-06 -4.47440573e-11 -2.60666650e-12 4.05691682e-11
1.02854415e-13 5.06010505e-14]
[-4.47440573e-11 3.55421812e-08 -1.12419138e-13 7.01361611e-13
2.48472282e-15 1.47630347e-15]
[-2.60666650e-12 -1.12419138e-13 3.54064429e-09 3.28338106e-14
1.40476883e-16 9.57434416e-17]
[ 4.05691682e-11 7.01361611e-13 3.28338106e-14 2.77952117e-06
-1.07037962e-11 -6.58710940e-12]
[ 1.02854415e-13 2.48472282e-15 1.40476883e-16 -1.07037962e-11
6.74801015e-09 -4.19586392e-14]
[ 5.06010505e-14 1.47630347e-15 9.57434416e-17 -6.58710940e-12
-4.19586392e-14 7.06457948e-09]]
LogLike[0390]=-3091.704136
sigma_a=0.143187
R:
[[24.7373502 0.49465576]
[ 0.49465576 25.99096158]]
m0:
[ 3.55547257 -1.80940783 0.1213661 -0.32091937 0.70995256 -0.68009785]
V0:
[[ 2.14765868e-06 -4.48807619e-11 -2.61663571e-12 4.06808015e-11
1.03183027e-13 5.07879782e-14]
[-4.48807620e-11 3.55421761e-08 -1.12918689e-13 7.03602512e-13
2.49386670e-15 1.48251256e-15]
[-2.61663571e-12 -1.12918689e-13 3.54064422e-09 3.29551174e-14
1.41067726e-16 9.61984399e-17]
[ 4.06808015e-11 7.03602512e-13 3.29551174e-14 2.77951238e-06
-1.07363013e-11 -6.61223621e-12]
[ 1.03183027e-13 2.49386670e-15 1.41067726e-16 -1.07363013e-11
6.74800997e-09 -4.21448578e-14]
[ 5.07879782e-14 1.48251256e-15 9.61984399e-17 -6.61223621e-12
-4.21448578e-14 7.06457918e-09]]
LogLike[0391]=-3091.684089
sigma_a=0.142915
R:
[[24.73738813 0.49445543]
[ 0.49445543 25.99162281]]
m0:
[ 3.55547237 -1.80940784 0.1213661 -0.32091915 0.70995256 -0.68009785]
V0:
[[ 2.14765321e-06 -4.50176426e-11 -2.62662432e-12 4.07924606e-11
1.03511925e-13 5.09751934e-14]
[-4.50176426e-11 3.55421711e-08 -1.13419532e-13 7.05845365e-13
2.50302451e-15 1.48873525e-15]
[-2.62662432e-12 -1.13419532e-13 3.54064414e-09 3.30766109e-14
1.41659864e-16 9.66547363e-17]
[ 4.07924606e-11 7.05845365e-13 3.30766109e-14 2.77950360e-06
-1.07688478e-11 -6.63741162e-12]
[ 1.03511925e-13 2.50302451e-15 1.41659864e-16 -1.07688478e-11
6.74800979e-09 -4.23315566e-14]
[ 5.09751934e-14 1.48873525e-15 9.66547363e-17 -6.63741162e-12
-4.23315566e-14 7.06457888e-09]]
LogLike[0392]=-3091.665863
sigma_a=0.142667
R:
[[24.73742651 0.49425537]
[ 0.49425537 25.99228403]]
m0:
[ 3.55547218 -1.80940784 0.1213661 -0.32091893 0.70995256 -0.68009785]
V0:
[[ 2.14764775e-06 -4.51546990e-11 -2.63663232e-12 4.09041439e-11
1.03841103e-13 5.11626931e-14]
[-4.51546990e-11 3.55421660e-08 -1.13921666e-13 7.08090132e-13
2.51219610e-15 1.49497143e-15]
[-2.63663232e-12 -1.13921666e-13 3.54064407e-09 3.31982891e-14
1.42253286e-16 9.71123241e-17]
[ 4.09041439e-11 7.08090132e-13 3.31982891e-14 2.77949481e-06
-1.08014355e-11 -6.66263555e-12]
[ 1.03841103e-13 2.51219610e-15 1.42253286e-16 -1.08014355e-11
6.74800961e-09 -4.25187351e-14]
[ 5.11626931e-14 1.49497143e-15 9.71123241e-17 -6.66263555e-12
-4.25187351e-14 7.06457858e-09]]
LogLike[0393]=-3091.650603
sigma_a=0.142460
R:
[[24.73746208 0.49407232]
[ 0.49407232 25.99288975]]
m0:
[ 3.55547198 -1.80940785 0.12136611 -0.32091871 0.70995256 -0.68009785]
V0:
[[ 2.14764227e-06 -4.52919156e-11 -2.64665801e-12 4.10158451e-11
1.04170525e-13 5.13504451e-14]
[-4.52919156e-11 3.55421608e-08 -1.14424980e-13 7.10336560e-13
2.52137992e-15 1.50121971e-15]
[-2.64665801e-12 -1.14424980e-13 3.54064399e-09 3.33201311e-14
1.42847859e-16 9.75710751e-17]
[ 4.10158451e-11 7.10336560e-13 3.33201311e-14 2.77948601e-06
-1.08340607e-11 -6.68790376e-12]
[ 1.04170525e-13 2.52137992e-15 1.42847859e-16 -1.08340607e-11
6.74800943e-09 -4.27063519e-14]
[ 5.13504451e-14 1.50121971e-15 9.75710751e-17 -6.68790376e-12
-4.27063519e-14 7.06457828e-09]]
LogLike[0394]=-3091.630143
sigma_a=0.142181
R:
[[24.73749238 0.49391815]
[ 0.49391815 25.99340043]]
m0:
[ 3.55547178 -1.80940785 0.12136611 -0.32091849 0.70995257 -0.68009785]
V0:
[[ 2.14763680e-06 -4.54292668e-11 -2.65669856e-12 4.11275572e-11
1.04500140e-13 5.15384025e-14]
[-4.54292668e-11 3.55421557e-08 -1.14929286e-13 7.12584304e-13
2.53057370e-15 1.50747794e-15]
[-2.65669856e-12 -1.14929286e-13 3.54064391e-09 3.34421065e-14
1.43443380e-16 9.80307892e-17]
[ 4.11275572e-11 7.12584304e-13 3.34421065e-14 2.77947721e-06
-1.08667174e-11 -6.71320914e-12]
[ 1.04500140e-13 2.53057370e-15 1.43443380e-16 -1.08667174e-11
6.74800925e-09 -4.28943371e-14]
[ 5.15384025e-14 1.50747794e-15 9.80307892e-17 -6.71320914e-12
-4.28943371e-14 7.06457798e-09]]
LogLike[0395]=-3091.607275
sigma_a=0.141870
R:
[[24.73753301 0.49371342]
[ 0.49371342 25.99407937]]
m0:
[ 3.55547159 -1.80940786 0.12136611 -0.32091828 0.70995257 -0.68009785]
V0:
[[ 2.14763132e-06 -4.55667993e-11 -2.66675916e-12 4.12393067e-11
1.04830081e-13 5.17266744e-14]
[-4.55667993e-11 3.55421506e-08 -1.15434930e-13 7.14834271e-13
2.53978273e-15 1.51375082e-15]
[-2.66675916e-12 -1.15434930e-13 3.54064384e-09 3.35642861e-14
1.44040294e-16 9.84918917e-17]
[ 4.12393067e-11 7.14834272e-13 3.35642861e-14 2.77946840e-06
-1.08994168e-11 -6.73856477e-12]
[ 1.04830081e-13 2.53978273e-15 1.44040294e-16 -1.08994168e-11
6.74800907e-09 -4.30828196e-14]
[ 5.17266744e-14 1.51375082e-15 9.84918917e-17 -6.73856477e-12
-4.30828196e-14 7.06457768e-09]]
LogLike[0396]=-3091.588856
sigma_a=0.141619
R:
[[24.73757946 0.49348274]
[ 0.49348274 25.99484538]]
m0:
[ 3.5554714 -1.80940786 0.12136611 -0.32091806 0.70995257 -0.68009785]
V0:
[[ 2.14762584e-06 -4.57045356e-11 -2.67684227e-12 4.13510906e-11
1.05160366e-13 5.19152870e-14]
[-4.57045356e-11 3.55421455e-08 -1.15942077e-13 7.17086583e-13
2.54900826e-15 1.52003975e-15]
[-2.67684227e-12 -1.15942077e-13 3.54064376e-09 3.36866869e-14
1.44638736e-16 9.89545252e-17]
[ 4.13510906e-11 7.17086583e-13 3.36866869e-14 2.77945959e-06
-1.09321641e-11 -6.76397684e-12]
[ 1.05160366e-13 2.54900826e-15 1.44638736e-16 -1.09321641e-11
6.74800889e-09 -4.32718610e-14]
[ 5.19152870e-14 1.52003975e-15 9.89545252e-17 -6.76397684e-12
-4.32718610e-14 7.06457738e-09]]
LogLike[0397]=-3091.573385
sigma_a=0.141407
R:
[[24.73761795 0.49329429]
[ 0.49329429 25.9954719 ]]
m0:
[ 3.55547121 -1.80940787 0.12136611 -0.32091784 0.70995257 -0.68009785]
V0:
[[ 2.14762036e-06 -4.58424364e-11 -2.68694361e-12 4.14628862e-11
1.05490881e-13 5.21041484e-14]
[-4.58424364e-11 3.55421403e-08 -1.16450442e-13 7.19340469e-13
2.55824583e-15 1.52634079e-15]
[-2.68694361e-12 -1.16450442e-13 3.54064368e-09 3.38092492e-14
1.45238330e-16 9.94183347e-17]
[ 4.14628862e-11 7.19340469e-13 3.38092492e-14 2.77945077e-06
-1.09649499e-11 -6.78943445e-12]
[ 1.05490881e-13 2.55824583e-15 1.45238330e-16 -1.09649499e-11
6.74800871e-09 -4.34613546e-14]
[ 5.21041484e-14 1.52634079e-15 9.94183347e-17 -6.78943445e-12
-4.34613546e-14 7.06457707e-09]]
LogLike[0398]=-3091.551751
sigma_a=0.141112
R:
[[24.7376507 0.49313566]
[ 0.49313566 25.99599982]]
m0:
[ 3.55547101 -1.80940787 0.12136611 -0.32091762 0.70995257 -0.68009785]
V0:
[[ 2.14761487e-06 -4.59804759e-11 -2.69706031e-12 4.15746929e-11
1.05821594e-13 5.22932212e-14]
[-4.59804759e-11 3.55421352e-08 -1.16959835e-13 7.21595705e-13
2.56749367e-15 1.53265211e-15]
[-2.69706031e-12 -1.16959835e-13 3.54064361e-09 3.39319489e-14
1.45838903e-16 9.98831415e-17]
[ 4.15746929e-11 7.21595705e-13 3.39319489e-14 2.77944195e-06
-1.09977681e-11 -6.81493045e-12]
[ 1.05821594e-13 2.56749367e-15 1.45838903e-16 -1.09977681e-11
6.74800853e-09 -4.36512297e-14]
[ 5.22932212e-14 1.53265211e-15 9.98831415e-17 -6.81493045e-12
-4.36512297e-14 7.06457677e-09]]
LogLike[0399]=-3091.532695
sigma_a=0.140851
R:
[[24.73769649 0.49291613]
[ 0.49291613 25.99673126]]
m0:
[ 3.55547082 -1.80940788 0.12136611 -0.3209174 0.70995257 -0.68009784]
V0:
[[ 2.14760938e-06 -4.61187103e-11 -2.70719860e-12 4.16865411e-11
1.06152662e-13 5.24826350e-14]
[-4.61187103e-11 3.55421300e-08 -1.17470672e-13 7.23853356e-13
2.57675802e-15 1.53897931e-15]
[-2.70719860e-12 -1.17470672e-13 3.54064353e-09 3.40548699e-14
1.46440987e-16 1.00349456e-16]
[ 4.16865411e-11 7.23853356e-13 3.40548699e-14 2.77943312e-06
-1.10306322e-11 -6.84048063e-12]
[ 1.06152662e-13 2.57675802e-15 1.46440987e-16 -1.10306322e-11
6.74800835e-09 -4.38416421e-14]
[ 5.24826350e-14 1.53897931e-15 1.00349456e-16 -6.84048063e-12
-4.38416421e-14 7.06457646e-09]]
Plots
Convergence
fig = go.Figure()
trace = go.Scatter(x=optim_res_ga["elapsed_time"], y=optim_res_ga["log_like"],
name="Gradient ascent", mode="lines+markers")
fig.add_trace(trace)
trace = go.Scatter(x=optim_res_em["elapsed_time"], y=optim_res_em["log_like"],
name="EM", mode="lines+markers")
fig.add_trace(trace)
fig.update_layout(xaxis_title="Elapsed Time (sec)",
yaxis_title="Log Likelihood")
fig
\(\sigma_a^2\) estimates
fig = go.Figure()
trace = go.Bar(x=["Gradient Ascent", "EM"],
y=[optim_res_ga["estimates"]["sigma_a"].item()**2,
optim_res_em["estimates"]["sigma_a"]**2])
fig.add_trace(trace)
fig.add_hline(y=sigma_a_true**2)
fig.update_layout(xaxis_title="Estimation Method",
yaxis_title=r'$\sigma_a^2$')
fig
\(\sigma_x^2\) estimates
fig = go.Figure()
trace = go.Bar(x=["Gradient Ascent", "EM"],
y=[optim_res_ga["estimates"]["pos_x_R_std"].item()**2,
optim_res_em["estimates"]["R"][0, 0]])
fig.add_trace(trace)
fig.add_hline(y=sqrt_diag_R[0]**2)
fig.update_layout(xaxis_title="Estimation Method",
yaxis_title=r'$\sigma_x$')
fig
\(m_0[0]\) estimates
fig = go.Figure()
trace = go.Bar(x=["Gradient Ascent", "EM"],
y=[optim_res_ga["estimates"]["m0"][0].item(),
optim_res_em["estimates"]["m0"][0]])
fig.add_trace(trace)
fig.add_hline(y=m0[0])
fig.update_layout(xaxis_title="Estimation Method",
yaxis_title=r'$m_0[0]$')
fig
\(V_0[0,0]\) estimates
fig = go.Figure()
trace = go.Bar(x=["Gradient Ascent", "EM"],
y=[optim_res_ga["estimates"]["sqrt_diag_V0"][0].item()**2,
optim_res_em["estimates"]["V0"][0, 0]])
fig.add_trace(trace)
fig.add_hline(y=sqrt_diag_V0[0]**2)
fig.update_layout(xaxis_title="Estimation Method",
yaxis_title=r'$V_0[0, 0]$')
fig
Total running time of the script: ( 1 minutes 52.004 seconds)
