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

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