Bayesian Model Choice Via Markov Chain Monte Carlo Methods
Journal of the Royal Statistical Society Series B (Statistical Methodology)Published 1 September 1995
Bradley P. Carlin, Siddhartha Chib
Citations1,017
SJR quartileQ1
SJR score3.31
SNIP2.48
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TL;DR
This paper presents a framework for Bayesian model choice, along with an MCMC algorithm that does not suffer from convergence difficulties, and applies equally well to problems where only one model is contemplated but its proper size is not known at the outset.
Abstract
SUMMARY Markov chain Monte Carlo (MCMC) integration methods enable the fitting of models of virtually unlimited complexity, and as such have revolutionized the practice of Bayesian data analysis. However, comparison across models may not proceed in a completely analogous fashion, owing to violations of the conditions sufficient to ensure convergence of the Markov chain. In this paper we present a framework for Bayesian model choice, along with an MCMC algorithm that does not suffer from convergence difficulties. Our algorithm applies equally well to problems where only one model is contemplated but its proper size is not known at the outset, such as problems involving integer-valued parameters, multiple changepoints or finite mixture distributions. We illustrate our approach with two published examples.
Keywords
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