Approximate Bayesian Computation (ABC) in practice

• Published in: Trends in ecology & evolution (Amsterdam) Vol. 25; no. 7; pp. 410 - 418
• Main Authors: Csilléry, Katalin, Blum, Michael G.B., Gaggiotti, Oscar E., François, Olivier
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2010; Elsevier
• Subjects: Africa; Algorithms; Animal, plant and microbial ecology; Animals; Bayes Theorem; Biodiversity; Biological and medical sciences; Biological Evolution; Biostatistics; Demography; Drosophila melanogaster; Drosophila melanogaster - genetics; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Life Sciences; Methods and techniques (sampling, tagging, trapping, modelling...); Models, Genetic; Populations and Evolution; Africa; Bayes estimation; Evolutionary ecology; Data analysis; Approximate method; Evolutionary biology; Growth; Biological evolution; Ecology; Models; Review; Method; Algorithm
• ISSN: 0169-5347; 1872-8383
• DOI: 10.1016/j.tree.2010.04.001

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.