Approximate Bayesian computation in evolution and ecology

Research output: Contribution to journalArticle (Academic Journal)peer-review

597 Citations (Scopus)

Abstract

In the past 10 years a statistical technique, approximate Bayesian computation (ABC), has been developed that can be used to infer parameters and choose between models in the complicated scenarios that are often considered in the environmental sciences For example, based on gene sequence and microsatellite clan, the method has been used to choose between competing models of human demographic history as well as to infer growth rates, times of divergence, and other parameters The method fits naturally in the Bayesian inferential framework, and a brief overview is given of the key concepts Three main approaches to ABC have been developed, and these are described and compared Although the method arose in population genetics, ABC is increasingly used in other fields, including epidemiology, systems biology, ecology, and agent-based modeling, and many of these applications ire briefly described
Translated title of the contributionApproximate Bayesian computation in evolution and ecology
Original languageEnglish
Pages (from-to)379 - 406
JournalAnnual Review of Ecology, Evolution, and Systematics
Volume41
DOIs
Publication statusPublished - 2010

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