Approximate Bayesian computation

Mark A. Beaumont*

*Corresponding author for this work

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

79 Citations (Scopus)


Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

Original languageEnglish
Pages (from-to)379-403
Number of pages25
JournalAnnual Review of Statistics and Its Application
Early online date28 Nov 2018
Publication statusPublished - 7 Mar 2019


  • Bayesian
  • intractable likelihood
  • Monte Carlo


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