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Sequential MCMC for Bayesian model selection

C. Andrieu‚ Nando De Freitas and Arnaud Doucet


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 model order estimation of autoregressive (AR) time series observed in additive noise

Book Title
IEEE Signal Processing Workshop on Higher−Order Statistics