Sequential MCMC for Bayesian model selection

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.

• Publication year

  1999

• Venue

  Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics. SPW-HOS '99

• Publication date

  1999-06-14

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/HOST.1999.778709
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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