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Approximate Bayesian Computation (ABC) in practice
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DOI：10.1016/j.tree.2010.04.001.png)
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Abstract
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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.
Keywords:
CHAIN MONTE-CARLO
DNA-SEQUENCE DATA
GENETIC DIVERSITY
MODEL SELECTION
DROSOPHILA-MELANOGASTER
STATISTICAL EVALUATION
COALESCENT SIMULATION
DEMOGRAPHIC HISTORY
POPULATION HISTORY
DYNAMICAL-SYSTEMS
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