Model Choice using Reversible Jump Markov Chain

We review the across-model simulation approach to computation for Bayesian model determination, based on the reversible jump Markov chain Monte Carlo method. Advantages, difficulties and variations of the methods are discussed. We also discuss some limitations of the ideal Bayesian view of the model determination problem, for which no computational methods can provide a cure. Some key words: Across-model sampling, Bayes factors, Bayesian model determination, posterior model probabilities, transdimensional inference, variable dimension problems,

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