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An Invitation to Sequential Monte Carlo Samplers

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  • Chenguang Dai
  • Jeremy Heng
  • Pierre E. Jacob
  • Nick Whiteley

Abstract

Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits. Supplementary materials for this article are available online.

Suggested Citation

  • Chenguang Dai & Jeremy Heng & Pierre E. Jacob & Nick Whiteley, 2022. "An Invitation to Sequential Monte Carlo Samplers," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1587-1600, September.
    • Handle: RePEc:taf:jnlasa:v:117:y:2022:i:539:p:1587-1600
    DOI: 10.1080/01621459.2022.2087659
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