Computing Marginal Likelihoods via Posterior Sampling
Stephen Graham Walker · Communications in Statistics - Simulation and Computation · 2013
This article presents a novel and simple approach to the estimation of a marginal likelihood, in a Bayesian context. The estimate is based on a Markov chain output which provides samples from the posterior distribution and an additional latent variable. It is the mean of this latent variable which provides the estimate for the value of the marginal likelihood.