Fundamentals and Recent Developments in Approximate Bayesian Computation

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

doi:10.1093/sysbio/syw077

Fundamentals and Recent Developments in Approximate Bayesian Computation

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

Research output: Contribution to journal › Article › Scientific › peer-review

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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.

Original language	English
Pages (from-to)	e66-e82
Journal	SYSTEMATIC BIOLOGY
Volume	66
Issue number	1
DOIs	https://doi.org/10.1093/sysbio/syw077
Publication status	Published - 2016
MoE publication type	A1 Journal article-refereed

Keywords

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

Access to Document

10.1093/sysbio/syw077

syw077Final published version, 1.09 MBLicence: CC BY

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author = "Jarno Lintusaari and Michael Gutmann and Ritabrata Dutta and Samuel Kaski and Jukka Corander",

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KW - Treebased models

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Fundamentals and Recent Developments in Approximate Bayesian Computation

Abstract

Keywords

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