Monte Carlo estimation of Bayesian credible and HPD intervals

This article considers how to estimate Bayesian credible and highest probab ility density (HPD) intervals for parameters of interest and provides a sim ple Monte Carlo approach to approximate these Bayesian intervals when a sam ple of the relevant parameters can be generated from their respective margi nal posterior distribution using a Markov chain Monte Carlo (MCMC) sampling algorithm. We also develop a Monte Carlo method to compute HPD intervals f or the parameters of interest from the desired posterior distribution using a sample from an importance sampling distribution. We apply our methodolog y to a Bayesian hierarchical model that has a posterior density containing analytically intractable integrals that depend on the (hyper) parameters. W e further show that our methods are useful not only for calculating the HPD intervals for the parameters of interest but also for computing the HPD in tervals for functions of the parameters. Necessary theory is developed and illustrative examples-including a simulation study-are given.