Sequential MCMC for Bayesian model selection
Andrieu, C.; De Freitas, N.; Doucet, A.
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Volume , Issue , 1999 Page(s):130 - 134
Digital Object Identifier   10.1109/HOST.1999.778709
Summary: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

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