Fundamentals and Recent Developments in Approximate Bayesian Computation
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2016
Department
Major/Subject
Mcode
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Language
en
Pages
e66-e82
Series
SYSTEMATIC BIOLOGY, Volume 66, issue 1
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 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.Description
Keywords
ABC, Approximate Bayesian computation, Bayesian inference, Likelihood-free inference, Phylogenetics, Simulator-based models, Stochastic simulation models, Treebased models
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Citation
Lintusaari, J, Gutmann, M, Dutta, R, Kaski, S & Corander, J 2016, ' Fundamentals and Recent Developments in Approximate Bayesian Computation ', SYSTEMATIC BIOLOGY, vol. 66, no. 1, pp. e66-e82 . https://doi.org/10.1093/sysbio/syw077
