Lintusaari , J , Gutmann , M U , Dutta , R , Kaski , S & Corander , J 2017 , ' Fundamentals and Recent Developments in Approximate Bayesian Computation ' , Systematic Biology , vol. 66 , no. 1 , pp. E66-E82 . https://doi.org/10.1093/sysbio/syw077
Title: | Fundamentals and Recent Developments in Approximate Bayesian Computation |
Author: | Lintusaari, Jarno; Gutmann, Michael U.; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka |
Contributor organization: | Helsinki Institute for Information Technology Department of Mathematics and Statistics Jukka Corander / Principal Investigator |
Date: | 2017-01 |
Language: | eng |
Number of pages: | 17 |
Belongs to series: | Systematic Biology |
ISSN: | 1063-5157 |
DOI: | https://doi.org/10.1093/sysbio/syw077 |
URI: | http://hdl.handle.net/10138/312716 |
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. |
Subject: |
ABC
approximate Bayesian computation Bayesian inference likelihood-free inference phylogenetics simulator-based models stochastic simulation models tree-based models MONTE-CARLO INDIRECT INFERENCE MODEL SELECTION EVOLUTION LIKELIHOODS SYSTEMS STATISTICS PARAMETERS 1181 Ecology, evolutionary biology 112 Statistics and probability |
Peer reviewed: | Yes |
Rights: | cc_by |
Usage restriction: | openAccess |
Self-archived version: | publishedVersion |
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