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Table 6 Posterior summary matrices of the simulation due to sampling importance resampling algorithm using the same function

From: Bayesian analysis of generalized log-Burr family with R

Logistic model (k =1)
Parameter Mean SD MCSE ESS LB Median UB
Beta[1] 62.73 6.34 0.06 10000 50.08 62.84 75.23
Beta[2] -17.30 1.81 0.02 10000 -20.88 -17.33 -13.66
Log.sigma -0.14 0.10 0.00 10000 -0.32 -0.14 0.06
Deviance 283.20 2.46 0.02 10000 280.38 282.56 289.75
LP -160.93 1.23 0.01 10000 -164.20 -160.61 -159.52
Sigma 0.88 0.09 0.00 10000 0.72 0.87 1.06
Weibull model (k =30)
Parameter Mean SD MCSE ESS LB Median UB
Beta[1] 65.18 5.86 0.06 10000 53.79 65.20 76.77
Beta[2] -17.83 1.67 0.02 10000 -21.16 -17.83 -14.58
Log.sigma 0.25 0.09 0.00 10000 0.08 0.25 0.43
Deviance 278.35 2.40 0.02 10000 275.57 277.72 284.52
LP -158.50 1.20 0.01 10000 -161.59 -158.19 -157.11
Sigma 1.29 0.11 0.00 10000 1.09 1.29 1.54