Estimation of posterior density functions from a posterior sample

Abstract

The joint posterior density function of parameters and marginal posterior density functions of subsets of parameters are key quantities in Bayesian inference. Even when the posterior densities are unknown, there are many cases where Markov Chain Monte Carlo methods can generate samples from the joint posterior distribution. This paper proposes a simple and efficient method of estimating the posterior density functions at various points simultaneously by using a posterior sample.