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Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination

Peter J. Green
Biometrika
Vol. 82, No. 4 (Dec., 1995), pp. 711-732
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2337340
Stable URL: http://www.jstor.org/stable/2337340
Page Count: 22
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Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination
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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 fixed 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 fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible 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 to a Bayesian comparison of binomial experiments.

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