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Sequential Monte Carlo Samplers

Del Moral, Pierre, Doucet, Arnaud, Jasra, Ajay·2006·Journal of the Royal Statistical Society Series B: Statistical Methodology
DOI 10.1111/j.1467-9868.2006.00553.x
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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.

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Research works printed but not linked: 38

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Metadata

Authors
Del Moral, P., Doucet, A., & Jasra, A.
Affiliations
  • Université Nice Sophia Antipolis , France
  • University of British Columbia , Vancouver , Canada
  • University of Cambridge , UK
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Publisher
Oxford University Press (OUP)
Published
2006-05-15
Volume
68
Issue
3
Pages
411-436
ISSN
  • 1369-7412
  • 1467-9868
DOI
10.1111/j.1467-9868.2006.00553.x
Cited by
1,239
References
0
Language
en
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)

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