Sequential MCMC for Bayesian model selection

Conference item

In this paper, we address the problem of sequential Bayesian model selection. This problem does not usually admit any closed-form analytical solution. We propose here an original sequential simulation-based method to solve the associated Bayesian computational problems. This method combines sequential importance sampling, a resampling procedure and reversible jump MCMC (Markov chain Monte Carlo) moves. We describe a generic algorithm and then apply it to the problem of sequential Bayesian mod...

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Authors: Andrieu, C; De Freitas, N; Doucet, A

• Host title: IEEE Signal Processing Workshop on Higher−Order Statistics
• Publication date: 1999-01-01
• DOI: 10.1109/HOST.1999.778709