Louis/Thèse/Lectures/@csilleryApproximateBayesianComputation2010.md

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literature note Animals Models Genetic Bayes Theorem Biodiversity Africa Algorithms Biological Evolution Biostatistics Demography Drosophila melanogaster	Note de lecture de *Approximate Bayesian Computation (ABC) in practice* de Katalin Csilléry, Michael G. B. Blum, Oscar E. Gaggiotti, Olivier François.	../these_ref.bib

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FirstAuthor:: Csilléry, Katalin
Author:: Blum, Michael G. B.
Author:: Gaggiotti, Oscar E.
Author:: François, Olivier

Title:: Approximate Bayesian Computation (ABC) in practice

Year:: 2010

Citekey:: csilleryApproximateBayesianComputation2010

itemType:: journalArticle

Journal:: Trends in Ecology & Evolution

Volume:: 25

Issue:: 7

Pages:: 410-418

DOI:: 10.1016/j.tree.2010.04.001
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Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.

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