Fundamentals and Recent Developments in Approximate Bayesian Computation

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

Research output: Contribution to journalArticlepeer-review

Abstract

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 obtain algorithms for approximate inference that make a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of approximate Bayesian computation, review the classical algorithms, and highlight recent developments.
Original languageEnglish
Pages (from-to)e66-e82
Number of pages17
JournalSystematic biology
Volume66
Issue number1
Early online date19 Oct 2016
DOIs
Publication statusPublished - 1 Jan 2017

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