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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