Fundamentals and Recent Developments in Approximate Bayesian Computation

Jarno Lintusaari, Michael Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

Research output: Contribution to journalArticleScientificpeer-review

70 Citations (Scopus)
180 Downloads (Pure)


Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible.We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments.
Original languageEnglish
Pages (from-to)e66-e82
Issue number1
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed


  • ABC
  • Approximate Bayesian computation
  • Bayesian inference
  • Likelihood-free inference
  • Phylogenetics
  • Simulator-based models
  • Stochastic simulation models
  • Treebased models

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