Sequential Monte Carlo Samplers

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ID: 291629
2006
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Ranked #56 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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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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openalex_W2147357149 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pierre Del Moral, Arnaud Doucet, Ajay Jasra
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2006
DOI
10.1111/j.1467-9868.2006.00553.x
URL
https://academic.oup.com/jrsssb/article-pdf/68/3/411/49795343/jrsssb_68_3_411.pdf
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