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Fundamentals and Recent Developments in Approximate Bayesian Computation
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DOI：10.1093/sysbio/syw077.png)
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Abstract
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Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments.
Keywords:
ABC
approximate Bayesian computation
Bayesian inference
likelihood-free inference
phylogenetics
simulator-based models
stochastic simulation models
tree-based models
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