Approximate Bayesian Computation (ABC) in practice.
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.
Csilléry
Katalin
K
Laboratoire Techniques de l'Ingénierie Médicale et de la Complexité, Centre National de la Recherche Scientifique UMR5525, Université Joseph Fourier, 38706 La Tronche, France. kati.csillery@gmail.com
Blum
Michael G B
MG
Gaggiotti
Oscar E
OE
François
Olivier
O
D000349:Africa; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D044822:Biodiversity; D005075:Biological Evolution; D056808:Biostatistics; D003710:Demography; D004331:Drosophila melanogaster / Q000235:genetics*; D008957:Models, Genetic