On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

Abstract

We investigate the efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance-sampling function. The approach is generally applicable to multi-block parameter vector settings, does not require additional Markov Chain Monte Carlo (MCMC) sampling and is not dependent on the type of MCMC scheme used to sample from the posterior. The proposed approach is applied to normal regression models, finite normal mixtures and longitudinal Poisson models, and leads to accurate marginal likelihood estimates.

References in corpus (3)

• Expectation Propagation for approximate Bayesian inference
• Approximating the marginal likelihood in mixture models
• Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions

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