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Automating and evaluating reversible jump MCMC proposal distributions

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Abstract

The reversible jump Markov chain Monte Carlo (MCMC) sampler (Green in Biometrika 82:711–732, 1995) has become an invaluable device for Bayesian practitioners. However, the primary difficulty with the sampler lies with the efficient construction of transitions between competing models of possibly differing dimensionality and interpretation. We propose the use of a marginal density estimator to construct between-model proposal distributions. This provides both a step towards black-box simulation for reversible jump samplers, and a tool to examine the utility of common between-model mapping strategies. We compare the performance of our approach to well established alternatives in both time series and mixture model examples.

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Authors and Affiliations

  1. School of Mathematics and Statistics, University of New South Wales, Sydney, Australia

    Y. Fan, G. W. Peters & S. A. Sisson

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  1. Y. Fan
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  2. G. W. Peters
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  3. S. A. Sisson
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Correspondence to Y. Fan.

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Fan, Y., Peters, G.W. & Sisson, S.A. Automating and evaluating reversible jump MCMC proposal distributions. Stat Comput 19, 409 (2009). https://doi.org/10.1007/s11222-008-9101-z

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  • DOI: https://doi.org/10.1007/s11222-008-9101-z

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