Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

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ID: 289418
1995
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Ranked #101 of 188 articles by views in jurnal biometrika dan kependudukan

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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.
Reference Key
openalex_W2106706098 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peter J. Green
Journal jurnal biometrika dan kependudukan
Year 1995
DOI
10.1093/biomet/82.4.711
URL
https://doi.org/10.1093/biomet/82.4.711
Keywords Keywords not found

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