Table 2 Summary matrices of the simulation due to sampling importance resampling algorithm using the function LaplaceApproximation , where Mean stands for posterior mean, SD for posterior standard deviation, MCSE for Monte Carlo standard error, ESS , for effective sample size, and LB , Median , UB are 2.5%, 50%, 97.5% quantiles, respectively
From: Bayesian analysis of generalized log-Burr family with R
Logistic model (k =1) | |||||||
|---|---|---|---|---|---|---|---|
Parameter | Mean | SD | MCSE | ESS | LB | Median | UB |
Beta | 5.09 | 0.09 | 0.00 | 1000 | 4.93 | 5.09 | 5.27 |
Log.sigma | -0.93 | 0.14 | 0.00 | 1000 | -1.22 | -0.93 | -0.65 |
Deviance | 149.04 | 1.81 | 0.06 | 1000 | 147.24 | 148.45 | 153.94 |
LP | -86.02 | 0.90 | 0.03 | 1000 | -88.47 | -85.72 | -85.12 |
Sigma | 0.40 | 0.06 | 0.00 | 1000 | 0.29 | 0.39 | 0.52 |
Weibull model (k=30) | |||||||
Parameter | Mean | SD | MCSE | ESS | LB | Median | UB |
Beta | 5.22 | 0.09 | 0.00 | 1000 | 5.06 | 5.21 | 5.40 |
Log.sigma | -0.82 | 0.15 | 0.00 | 1000 | -1.10 | -0.82 | -0.51 |
Deviance | 149.44 | 1.94 | 0.06 | 1000 | 147.52 | 148.87 | 154.61 |
LP | -86.22 | 0.97 | 0.03 | 1000 | -88.80 | -85.93 | -85.26 |
Sigma | 0.45 | 0.07 | 0.00 | 1000 | 0.33 | 0.44 | 0.60 |