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bakfoo/rats_stan_advi_result.R

Created January 18, 2016 13:47
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rats_stan_advi_result.R
> print(rats_fit)
Inference for Stan model: rats.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
alpha[1] 239.90 0.07 2.70 234.72 238.03 239.90 241.71 245.11 1595 1.00
alpha[2] 247.89 0.06 2.78 242.43 246.02 247.94 249.74 253.40 2000 1.00
alpha[3] 252.53 0.06 2.76 247.12 250.71 252.57 254.35 257.86 1838 1.00
alpha[4] 232.53 0.06 2.71 227.16 230.69 232.56 234.40 237.75 1797 1.00
alpha[5] 231.60 0.06 2.62 226.59 229.97 231.59 233.27 236.93 2000 1.00
alpha[6] 249.58 0.06 2.63 244.22 247.83 249.57 251.35 254.68 2000 1.00
alpha[7] 228.63 0.06 2.81 222.95 226.77 228.68 230.57 234.03 2000 1.00
alpha[8] 248.40 0.07 2.79 243.12 246.46 248.40 250.28 253.91 1636 1.00
alpha[9] 283.27 0.07 2.78 277.81 281.39 283.28 285.16 288.79 1616 1.00
alpha[10] 219.32 0.07 2.77 214.03 217.42 219.31 221.10 224.97 1762 1.00
alpha[11] 258.18 0.06 2.71 252.82 256.43 258.15 259.96 263.67 1921 1.00
alpha[12] 228.06 0.06 2.66 222.79 226.18 228.10 229.86 233.11 1840 1.00
alpha[13] 242.42 0.06 2.71 236.98 240.58 242.45 244.27 247.52 2000 1.00
alpha[14] 268.16 0.06 2.74 262.59 266.33 268.20 269.91 273.39 2000 1.00
alpha[15] 242.86 0.06 2.57 237.60 241.21 242.78 244.60 247.85 2000 1.00
alpha[16] 245.26 0.06 2.75 239.73 243.41 245.35 247.24 250.29 2000 1.00
alpha[17] 232.17 0.06 2.76 226.90 230.27 232.08 234.05 237.63 2000 1.00
alpha[18] 240.45 0.06 2.73 235.33 238.57 240.38 242.29 245.76 2000 1.00
alpha[19] 253.87 0.06 2.76 248.27 252.07 253.89 255.71 259.20 1845 1.00
alpha[20] 241.71 0.06 2.78 236.05 239.88 241.69 243.58 247.23 2000 1.00
alpha[21] 248.68 0.08 2.78 243.13 246.90 248.64 250.63 254.07 1282 1.00
alpha[22] 225.29 0.06 2.69 220.07 223.48 225.31 227.08 230.48 2000 1.00
alpha[23] 228.64 0.06 2.76 223.24 226.78 228.67 230.47 234.15 2000 1.00
alpha[24] 245.00 0.06 2.66 239.73 243.19 244.99 246.85 250.20 1833 1.00
alpha[25] 234.48 0.06 2.77 229.08 232.68 234.51 236.29 239.72 2000 1.00
alpha[26] 253.84 0.06 2.70 248.31 252.10 253.87 255.63 259.09 1791 1.00
alpha[27] 254.39 0.06 2.66 249.32 252.57 254.39 256.18 259.55 2000 1.00
alpha[28] 242.98 0.06 2.61 237.90 241.23 242.97 244.70 248.30 2000 1.00
alpha[29] 217.99 0.06 2.67 212.72 216.28 217.95 219.71 223.40 2000 1.00
alpha[30] 241.42 0.06 2.71 236.16 239.69 241.31 243.12 246.81 1890 1.00
beta[1] 6.07 0.01 0.23 5.60 5.91 6.07 6.23 6.53 2000 1.00
beta[2] 7.05 0.01 0.25 6.57 6.89 7.05 7.22 7.54 1746 1.00
beta[3] 6.47 0.01 0.24 5.98 6.31 6.47 6.63 6.96 1440 1.00
beta[4] 5.35 0.01 0.26 4.86 5.18 5.34 5.51 5.86 1481 1.00
beta[5] 6.57 0.01 0.25 6.08 6.39 6.57 6.74 7.07 2000 1.00
beta[6] 6.18 0.01 0.24 5.71 6.03 6.18 6.34 6.65 2000 1.00
beta[7] 5.98 0.01 0.25 5.49 5.82 5.98 6.15 6.44 2000 1.00
beta[8] 6.41 0.01 0.24 5.93 6.25 6.41 6.57 6.87 2000 1.00
beta[9] 7.04 0.01 0.25 6.55 6.86 7.04 7.21 7.52 2000 1.00
beta[10] 5.85 0.01 0.24 5.40 5.68 5.85 6.02 6.32 1318 1.00
beta[11] 6.80 0.01 0.25 6.32 6.64 6.79 6.96 7.29 1478 1.00
beta[12] 6.12 0.01 0.23 5.68 5.96 6.11 6.28 6.56 2000 1.00
beta[13] 6.16 0.01 0.23 5.71 6.00 6.17 6.32 6.62 2000 1.00
beta[14] 6.69 0.01 0.25 6.21 6.52 6.70 6.85 7.15 1752 1.00
beta[15] 5.42 0.01 0.25 4.94 5.26 5.42 5.59 5.92 2000 1.00
beta[16] 5.92 0.01 0.24 5.45 5.75 5.91 6.08 6.40 2000 1.00
beta[17] 6.27 0.01 0.26 5.76 6.09 6.26 6.44 6.76 2000 1.00
beta[18] 5.85 0.01 0.24 5.38 5.69 5.85 6.01 6.30 1488 1.00
beta[19] 6.40 0.01 0.24 5.91 6.24 6.40 6.56 6.87 1907 1.00
beta[20] 6.06 0.01 0.24 5.58 5.90 6.06 6.23 6.53 1987 1.00
beta[21] 6.39 0.01 0.24 5.93 6.22 6.39 6.57 6.85 2000 1.00
beta[22] 5.86 0.01 0.24 5.38 5.70 5.87 6.02 6.35 2000 1.00
beta[23] 5.75 0.01 0.24 5.28 5.59 5.75 5.91 6.22 1724 1.00
beta[24] 5.89 0.01 0.23 5.44 5.73 5.89 6.04 6.35 1296 1.00
beta[25] 6.90 0.01 0.26 6.39 6.73 6.89 7.07 7.42 1740 1.00
beta[26] 6.55 0.01 0.24 6.07 6.39 6.55 6.71 7.01 2000 1.00
beta[27] 5.91 0.01 0.24 5.43 5.75 5.91 6.06 6.36 1662 1.00
beta[28] 5.85 0.01 0.24 5.36 5.69 5.84 6.01 6.34 2000 1.00
beta[29] 5.68 0.01 0.25 5.19 5.51 5.67 5.84 6.16 2000 1.00
beta[30] 6.13 0.01 0.25 5.63 5.97 6.13 6.30 6.64 1522 1.00
mu_alpha 242.41 0.07 2.75 236.96 240.59 242.42 244.28 247.74 1544 1.00
mu_beta 6.19 0.00 0.10 5.99 6.12 6.18 6.25 6.39 1193 1.01
sigmasq_y 37.50 0.22 5.70 28.30 33.39 36.85 40.93 50.19 676 1.00
sigmasq_alpha 216.76 1.67 63.62 125.24 173.22 205.91 248.30 367.55 1443 1.00
sigmasq_beta 0.27 0.00 0.10 0.13 0.20 0.25 0.32 0.51 972 1.00
sigma_y 6.11 0.02 0.46 5.32 5.78 6.07 6.40 7.08 678 1.00
sigma_alpha 14.58 0.05 2.07 11.19 13.16 14.35 15.76 19.17 1531 1.00
sigma_beta 0.51 0.00 0.09 0.36 0.45 0.50 0.56 0.72 877 1.00
alpha0 106.31 0.10 3.60 99.28 103.90 106.32 108.64 113.45 1288 1.00
lp__ -438.50 0.38 7.17 -453.94 -442.92 -438.19 -433.50 -425.77 361 1.02
Samples were drawn using NUTS(diag_e) at Mon Jan 18 22:38:34 2016.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
> print(rats_fit_vb)
Inference for Stan model: rats.
1 chains, each with iter=2000; warmup=0; thin=1;
post-warmup draws per chain=2000, total post-warmup draws=2000.
mean sd 2.5% 25% 50% 75% 97.5%
alpha[1] 6.89 5.93 -4.16 2.82 6.86 10.94 18.42
alpha[2] 7.36 5.90 -4.38 3.37 7.36 11.35 19.19
alpha[3] 6.77 4.82 -2.54 3.39 6.78 10.07 16.30
alpha[4] 7.02 6.63 -6.12 2.47 7.17 11.64 19.63
alpha[5] 6.78 4.48 -2.38 3.91 6.80 9.86 15.47
alpha[6] 6.74 5.40 -4.08 3.06 6.87 10.39 17.24
alpha[7] 6.67 5.91 -4.89 2.73 6.86 10.60 18.40
alpha[8] 7.04 6.50 -5.62 2.74 7.17 11.37 19.42
alpha[9] 6.40 5.62 -4.44 2.42 6.39 10.06 17.67
alpha[10] 7.07 6.58 -5.46 2.56 7.06 11.36 20.15
alpha[11] 6.89 5.79 -4.51 2.94 6.91 10.93 18.19
alpha[12] 7.04 6.83 -6.16 2.30 6.86 11.75 20.26
alpha[13] 6.77 4.72 -2.84 3.68 6.71 9.85 16.20
alpha[14] 6.91 6.03 -4.94 2.94 6.76 10.91 19.39
alpha[15] 7.09 4.47 -1.73 4.06 7.17 9.96 15.67
alpha[16] 6.66 5.96 -5.11 2.73 6.72 10.63 18.51
alpha[17] 6.94 6.10 -5.00 2.79 6.89 11.09 18.40
alpha[18] 6.84 5.92 -4.62 2.79 6.84 10.87 18.54
alpha[19] 6.85 5.19 -3.32 3.34 6.88 10.39 16.83
alpha[20] 7.11 5.22 -2.98 3.68 7.04 10.56 17.50
alpha[21] 6.50 5.08 -3.53 3.13 6.46 9.97 16.48
alpha[22] 6.93 4.77 -2.53 3.68 6.92 10.09 16.33
alpha[23] 6.77 5.34 -3.35 3.13 6.68 10.36 17.46
alpha[24] 6.45 5.27 -3.80 2.91 6.32 10.02 16.81
alpha[25] 6.95 5.31 -3.15 3.28 7.09 10.47 17.79
alpha[26] 6.96 5.96 -4.28 2.81 6.88 10.87 18.32
alpha[27] 6.75 5.20 -3.19 3.14 6.85 10.32 16.62
alpha[28] 6.67 5.99 -5.06 2.74 6.61 10.63 18.09
alpha[29] 6.55 5.11 -3.68 3.18 6.65 9.96 16.48
alpha[30] 6.61 4.34 -1.66 3.73 6.46 9.52 15.33
beta[1] -92.52 8.03 -108.75 -98.00 -92.44 -87.32 -75.92
beta[2] 4.42 1.80 0.82 3.25 4.39 5.63 7.80
beta[3] 4.48 1.77 0.90 3.32 4.46 5.67 7.87
beta[4] 4.58 1.91 0.89 3.28 4.52 5.88 8.26
beta[5] 4.37 1.90 0.80 3.08 4.35 5.63 8.26
beta[6] 4.21 1.75 0.86 2.98 4.20 5.37 7.69
beta[7] 4.58 1.88 0.92 3.28 4.63 5.82 8.15
beta[8] 4.47 1.72 1.01 3.31 4.51 5.63 7.76
beta[9] 4.33 1.72 0.90 3.14 4.34 5.51 7.66
beta[10] 4.48 2.44 -0.32 2.87 4.47 6.06 9.32
beta[11] 4.43 1.73 1.04 3.20 4.47 5.58 7.76
beta[12] 4.39 1.90 0.68 3.14 4.41 5.63 8.05
beta[13] 4.11 1.98 0.31 2.75 4.11 5.48 7.92
beta[14] 4.32 1.98 0.39 3.05 4.33 5.63 8.34
beta[15] 4.67 1.89 1.02 3.41 4.66 5.96 8.43
beta[16] 4.53 1.67 1.30 3.36 4.58 5.71 7.73
beta[17] 4.47 1.86 0.82 3.21 4.45 5.75 7.98
beta[18] 4.42 1.97 0.56 3.08 4.43 5.73 8.29
beta[19] 4.47 1.84 0.85 3.17 4.43 5.68 8.16
beta[20] 4.56 2.08 0.35 3.18 4.56 5.94 8.59
beta[21] 4.37 1.73 0.98 3.25 4.39 5.51 7.80
beta[22] 4.39 2.00 0.48 3.03 4.41 5.78 8.24
beta[23] 4.45 2.19 0.17 2.96 4.41 5.91 8.75
beta[24] 4.43 1.92 0.56 3.12 4.41 5.79 8.04
beta[25] 4.32 2.46 -0.46 2.62 4.23 5.98 9.21
beta[26] 4.48 2.11 0.38 3.09 4.50 5.91 8.50
beta[27] 4.25 1.72 0.86 3.10 4.28 5.44 7.61
beta[28] 4.48 1.96 0.57 3.17 4.51 5.81 8.19
beta[29] 4.39 2.12 0.25 2.98 4.44 5.88 8.37
beta[30] 3.94 2.00 0.12 2.59 3.96 5.33 7.62
mu_alpha 4.45 2.14 0.17 3.02 4.46 5.93 8.62
mu_beta 6.70 0.95 4.82 6.06 6.70 7.33 8.61
sigmasq_y 4.51 0.36 3.77 4.27 4.50 4.76 5.23
sigmasq_alpha 55599.79 6679.65 43931.65 51175.75 55096.70 59665.15 70135.51
sigmasq_beta 29.41 8.48 16.20 23.36 28.34 34.31 49.32
sigma_y 3.95 1.12 2.26 3.13 3.82 4.58 6.50
sigma_alpha 235.37 14.09 209.60 226.22 234.73 244.26 264.83
sigma_beta 5.37 0.76 4.02 4.83 5.32 5.86 7.02
alpha0 1.97 0.27 1.50 1.77 1.95 2.14 2.55
lp__ -92.52 8.03 -108.75 -98.00 -92.44 -87.32 -75.92
Approximate samples were drawn using VB(meanfield) at Mon Jan 18 22:38:34 2016.
We recommend genuine 'sampling' from the posterior distribution for final inferences!
> print(rats_fit_vbfr)
Inference for Stan model: rats.
1 chains, each with iter=2000; warmup=0; thin=1;
post-warmup draws per chain=2000, total post-warmup draws=2000.
mean sd 2.5% 25% 50% 75% 97.5%
alpha[1] 6.25 1.92 2.46 4.94 6.20 7.60 9.93
alpha[2] 5.98 2.03 1.86 4.60 6.02 7.34 9.87
alpha[3] 5.55 1.95 1.61 4.27 5.60 6.87 9.47
alpha[4] 6.17 1.97 2.29 4.85 6.16 7.46 10.14
alpha[5] 5.31 1.98 1.40 3.92 5.30 6.69 9.14
alpha[6] 6.08 2.04 2.24 4.69 6.08 7.46 10.15
alpha[7] 5.85 1.91 2.10 4.62 5.85 7.05 9.76
alpha[8] 5.19 2.00 1.27 3.85 5.15 6.51 9.12
alpha[9] 4.56 1.94 0.82 3.25 4.54 5.89 8.41
alpha[10] 5.41 1.98 1.49 4.05 5.44 6.78 9.32
alpha[11] 5.35 1.98 1.68 3.94 5.36 6.69 9.26
alpha[12] 6.02 2.01 2.11 4.61 6.04 7.32 10.06
alpha[13] 6.10 1.95 2.27 4.78 6.12 7.41 9.83
alpha[14] 5.87 2.01 2.02 4.55 5.89 7.23 9.74
alpha[15] 5.93 1.96 2.09 4.62 5.95 7.23 9.74
alpha[16] 5.60 1.91 1.85 4.29 5.62 6.88 9.35
alpha[17] 4.34 2.07 0.25 2.91 4.33 5.79 8.26
alpha[18] 6.07 1.98 2.21 4.76 6.09 7.32 9.99
alpha[19] 4.87 1.99 1.17 3.50 4.90 6.21 8.79
alpha[20] 6.27 2.04 2.12 4.90 6.27 7.65 10.30
alpha[21] 4.31 1.99 0.29 2.97 4.31 5.65 8.16
alpha[22] 5.30 1.97 1.39 4.09 5.31 6.54 9.34
alpha[23] 4.72 2.06 0.67 3.30 4.69 6.17 8.64
alpha[24] 4.31 2.01 0.41 2.95 4.34 5.69 8.25
alpha[25] 5.66 1.97 1.86 4.31 5.65 6.98 9.67
alpha[26] 5.98 1.95 2.21 4.62 5.99 7.31 9.90
alpha[27] 5.80 2.00 1.73 4.45 5.79 7.16 9.68
alpha[28] 5.43 1.89 1.79 4.16 5.43 6.72 9.20
alpha[29] 4.48 2.02 0.48 3.10 4.48 5.83 8.35
alpha[30] 4.47 1.96 0.60 3.16 4.51 5.77 8.20
beta[1] -91.14 10.15 -110.89 -98.23 -91.22 -84.35 -71.29
beta[2] 4.43 1.30 1.86 3.54 4.44 5.34 6.96
beta[3] 4.47 1.32 1.89 3.61 4.47 5.33 7.03
beta[4] 4.50 1.31 2.04 3.61 4.50 5.40 7.07
beta[5] 4.09 1.34 1.36 3.22 4.11 4.96 6.72
beta[6] 4.58 1.26 2.16 3.72 4.56 5.44 7.04
beta[7] 4.33 1.34 1.71 3.45 4.39 5.23 6.94
beta[8] 4.22 1.30 1.67 3.36 4.21 5.12 6.72
beta[9] 4.66 1.35 1.96 3.75 4.67 5.58 7.33
beta[10] 4.72 1.26 2.32 3.88 4.73 5.57 7.15
beta[11] 4.58 1.27 2.04 3.73 4.58 5.43 7.08
beta[12] 4.65 1.37 1.93 3.75 4.67 5.56 7.26
beta[13] 4.63 1.31 2.11 3.73 4.62 5.54 7.18
beta[14] 4.31 1.30 1.81 3.41 4.29 5.21 6.86
beta[15] 4.76 1.32 2.16 3.87 4.75 5.69 7.28
beta[16] 4.37 1.29 1.88 3.53 4.33 5.23 6.99
beta[17] 4.27 1.31 1.72 3.38 4.30 5.16 6.85
beta[18] 4.59 1.34 2.04 3.67 4.57 5.48 7.21
beta[19] 4.28 1.33 1.65 3.42 4.24 5.16 6.94
beta[20] 4.09 1.34 1.38 3.22 4.10 4.97 6.77
beta[21] 4.40 1.31 1.84 3.50 4.40 5.31 6.86
beta[22] 4.14 1.36 1.52 3.25 4.14 5.03 6.69
beta[23] 4.12 1.31 1.52 3.22 4.15 5.03 6.62
beta[24] 4.21 1.33 1.60 3.33 4.19 5.08 6.86
beta[25] 4.59 1.28 2.11 3.76 4.58 5.44 7.20
beta[26] 4.19 1.34 1.43 3.27 4.22 5.13 6.72
beta[27] 4.63 1.30 2.17 3.73 4.60 5.50 7.28
beta[28] 4.35 1.34 1.77 3.44 4.34 5.27 6.90
beta[29] 4.35 1.31 1.71 3.49 4.32 5.24 6.94
beta[30] 4.41 1.33 1.77 3.51 4.40 5.34 7.07
mu_alpha 4.31 1.35 1.56 3.43 4.32 5.22 6.90
mu_beta 5.43 0.59 4.26 5.04 5.43 5.85 6.55
sigmasq_y 4.39 0.46 3.49 4.08 4.40 4.71 5.29
sigmasq_alpha 57188.72 7912.12 43053.63 51645.88 56994.10 62274.22 74018.05
sigmasq_beta 4.72 1.47 2.51 3.66 4.51 5.52 8.23
sigma_y 1.83 0.56 0.97 1.44 1.74 2.14 3.13
sigma_alpha 238.57 16.50 207.49 227.26 238.73 249.55 272.06
sigma_beta 2.15 0.33 1.58 1.91 2.12 2.35 2.87
alpha0 1.34 0.20 0.98 1.20 1.32 1.46 1.77
lp__ -91.14 10.15 -110.89 -98.23 -91.22 -84.35 -71.29
Approximate samples were drawn using VB(fullrank) at Mon Jan 18 22:32:07 2016.
We recommend genuine 'sampling' from the posterior distribution for final inferences!
> print(rats_0)
Inference for Stan model: rats.
1 chains, each with iter=998; warmup=0; thin=1;
post-warmup draws per chain=998, total post-warmup draws=998.
mean sd 2.5% 25% 50% 75% 97.5%
alpha[1] 239.87 2.86 234.39 237.94 239.72 241.63 245.90
alpha[2] 247.71 2.80 242.24 245.77 247.70 249.60 252.96
alpha[3] 252.43 2.74 247.25 250.67 252.40 254.17 258.29
alpha[4] 232.67 2.88 227.04 230.70 232.70 234.70 237.91
alpha[5] 231.56 3.01 225.47 229.52 231.53 233.61 237.67
alpha[6] 249.52 2.76 244.20 247.65 249.54 251.41 255.01
alpha[7] 228.60 2.86 223.21 226.53 228.60 230.64 234.01
alpha[8] 248.46 2.90 242.89 246.47 248.53 250.37 254.17
alpha[9] 283.08 2.78 277.50 281.19 283.09 284.87 288.66
alpha[10] 219.35 2.88 213.88 217.39 219.35 221.31 224.79
alpha[11] 258.46 2.98 252.66 256.50 258.40 260.43 264.52
alpha[12] 228.28 3.02 222.21 226.25 228.31 230.27 234.14
alpha[13] 242.46 2.90 236.98 240.56 242.41 244.31 247.96
alpha[14] 268.23 2.90 262.75 266.20 268.23 270.26 273.78
alpha[15] 242.85 2.81 236.93 241.02 242.98 244.75 248.24
alpha[16] 245.19 2.99 239.25 243.24 245.24 247.10 251.07
alpha[17] 232.17 2.92 226.77 230.07 232.19 234.17 238.02
alpha[18] 240.50 2.74 234.95 238.63 240.44 242.30 245.56
alpha[19] 253.84 2.64 248.62 252.02 253.86 255.62 258.86
alpha[20] 241.63 2.74 236.54 239.77 241.58 243.53 246.74
alpha[21] 248.59 2.95 242.86 246.65 248.49 250.62 254.55
alpha[22] 225.14 2.71 220.01 223.35 225.08 226.85 230.82
alpha[23] 228.67 2.68 223.59 226.86 228.57 230.49 233.71
alpha[24] 245.28 2.89 239.47 243.41 245.38 247.23 250.78
alpha[25] 234.47 2.69 229.31 232.56 234.38 236.28 239.70
alpha[26] 254.00 2.82 248.12 252.17 254.00 256.01 259.15
alpha[27] 254.29 2.56 249.43 252.53 254.40 256.00 259.21
alpha[28] 242.93 2.90 237.69 240.91 242.90 244.90 248.74
alpha[29] 217.90 2.71 212.51 215.99 218.10 219.73 223.16
alpha[30] 241.51 2.85 236.11 239.56 241.40 243.47 247.02
beta[1] 106.31 3.69 99.14 103.65 106.33 109.01 112.96
beta[2] 6.06 0.25 5.56 5.90 6.08 6.23 6.53
beta[3] 7.09 0.26 6.58 6.93 7.10 7.26 7.61
beta[4] 6.51 0.25 6.02 6.36 6.51 6.67 6.99
beta[5] 5.33 0.26 4.82 5.17 5.32 5.51 5.81
beta[6] 6.57 0.24 6.11 6.41 6.57 6.74 7.06
beta[7] 6.21 0.25 5.67 6.04 6.20 6.37 6.70
beta[8] 5.98 0.24 5.51 5.82 5.98 6.14 6.45
beta[9] 6.43 0.26 5.93 6.26 6.42 6.61 6.91
beta[10] 7.05 0.24 6.55 6.88 7.05 7.21 7.47
beta[11] 5.84 0.25 5.35 5.68 5.84 6.01 6.33
beta[12] 6.83 0.26 6.29 6.67 6.84 7.01 7.34
beta[13] 6.12 0.25 5.62 5.95 6.12 6.30 6.60
beta[14] 6.16 0.24 5.70 6.01 6.17 6.33 6.61
beta[15] 6.68 0.26 6.16 6.50 6.69 6.86 7.18
beta[16] 5.43 0.24 4.96 5.27 5.43 5.61 5.91
beta[17] 5.89 0.26 5.39 5.70 5.89 6.06 6.42
beta[18] 6.33 0.25 5.83 6.15 6.33 6.51 6.78
beta[19] 5.84 0.26 5.32 5.68 5.84 6.00 6.36
beta[20] 6.41 0.25 5.91 6.25 6.40 6.57 6.92
beta[21] 6.05 0.25 5.55 5.88 6.05 6.23 6.55
beta[22] 6.36 0.27 5.85 6.18 6.36 6.54 6.90
beta[23] 5.84 0.26 5.31 5.67 5.83 6.01 6.33
beta[24] 5.77 0.25 5.27 5.60 5.78 5.95 6.24
beta[25] 5.93 0.27 5.39 5.76 5.92 6.11 6.46
beta[26] 6.89 0.24 6.42 6.73 6.90 7.06 7.37
beta[27] 6.55 0.25 6.05 6.39 6.55 6.72 7.01
beta[28] 5.94 0.26 5.43 5.76 5.94 6.11 6.45
beta[29] 5.85 0.28 5.30 5.66 5.85 6.05 6.38
beta[30] 5.67 0.26 5.15 5.50 5.67 5.84 6.19
mu_alpha 6.12 0.23 5.66 5.96 6.12 6.27 6.56
mu_beta 242.46 2.88 236.48 240.43 242.58 244.61 247.59
sigmasq_y 6.19 0.10 5.99 6.12 6.19 6.26 6.38
sigmasq_alpha 39.79 4.90 30.94 36.38 39.60 42.76 49.81
sigmasq_beta 219.64 65.24 121.81 174.99 209.61 254.33 378.62
sigma_y 0.30 0.08 0.17 0.25 0.29 0.35 0.49
sigma_alpha 6.30 0.39 5.56 6.03 6.29 6.54 7.06
sigma_beta 14.67 2.10 11.04 13.23 14.48 15.95 19.46
alpha0 0.55 0.07 0.41 0.50 0.54 0.59 0.70
lp__ 106.31 3.69 99.14 103.65 106.33 109.01 112.96
Approximate samples were drawn using VB(meanfield) at Mon Jan 18 22:39:03 2016.
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