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Sequential Monte Carlo samplers

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  • Pierre Del Moral
  • Arnaud Doucet
  • Ajay Jasra

Abstract

Summary. We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference.

Suggested Citation

  • Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    • Handle: RePEc:bla:jorssb:v:68:y:2006:i:3:p:411-436
    DOI: 10.1111/j.1467-9868.2006.00553.x
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/6215 is not listed on IDEAS
    2. Celeux, Gilles & Marin, Jean-Michel & Robert, Christian P., 2006. "Iterated importance sampling in missing data problems," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3386-3404, August.
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