DMCA
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination (1995)
| Venue: | Biometrika |
| Citations: | 1343 - 23 self |
BibTeX
@ARTICLE{Green95reversiblejump,
author = {Peter J. Green},
title = {Reversible jump Markov chain Monte Carlo computation and Bayesian model determination},
journal = {Biometrika},
year = {1995},
pages = {16}
}
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Abstract
Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not xed. This article proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of di ering dimensionality, which is exible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and toaBayesian comparison of binomial experiments.
Keyphrases
bayesian model determination wide applicability xed standard underlying measure markov chain monte carlo method model determination problem parameter vector joint distribution change-point analysis binomial experiment toabayesian comparison bayesian computation reversible markov chain sampler parameter subspace new framework
