Approximate Bayesian computation

PLoS Comput Biol. 2013;9(1):e1002803. doi: 10.1371/journal.pcbi.1002803. Epub 2013 Jan 10.

Abstract

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem*
  • Quality Control

Grant support

MS was supported by SystemsX.ch (RTD project YeastX). AGB was supported by SystemsX.ch (RTD projects YeastX and LiverX). JC was supported by the European Research Council grant no. 239784. EN was supported by the FICS graduate school. MF was supported by a Swiss NSF grant No 3100-126074 to Laurent Excoffier. CD was supported by SNSF advanced researcher fellowship no. 136461. The funders had no role in the preparation of the manuscript.