Bp. Carlin et S. Chib, BAYESIAN MODEL CHOICE VIA MARKOV-CHAIN MONTE-CARLO METHODS, Journal of the Royal Statistical Society. Series B: Methodological, 57(3), 1995, pp. 473-484
Citations number
26
Categorie Soggetti
Statistic & Probability","Statistic & Probability
Journal title
Journal of the Royal Statistical Society. Series B: Methodological
Markov chain Monte Carlo (MCMC) integration methods enable the fitting
of models of virtually unlimited complexity, and as such have revolut
ionized the practice of Bayesian data analysis. However, comparison ac
ross models may not proceed in a completely analogous fashion, owing t
o 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 conver
gence difficulties. Our algorithm applies equally well to problems whe
re only one model is contemplated but its proper size is not known at
the outset, such as problems involving integer-valued parameters, mult
iple changepoints or finite mixture distributions. We illustrate our a
pproach with two published examples.