An Invitation to Sequential Monte Carlo Samplers

Chenguang Dai, Jeremy Heng, Pierre e. Jacob, Nick Whiteley

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)
2 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1587-1600
JournalJournal of the American Statistical Association
Volume117
Issue number539
Early online date7 Jul 2022
DOIs
Publication statusPublished - 1 Sept 2022

Bibliographical note

37 pages, 8 figures; small typos corrected

Keywords

  • stat.CO
  • stat.ME

Fingerprint

Dive into the research topics of 'An Invitation to Sequential Monte Carlo Samplers'. Together they form a unique fingerprint.

Cite this