UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Approximate Bayesian computation.

Sunnåker, M; Busetto, AG; Numminen, E; Corander, J; Foll, M; Dessimoz, C; (2013) Approximate Bayesian computation. PLoS Computational Biology , 9 (1) , Article e1002803. 10.1371/journal.pcbi.1002803. Green open access

[img]
Preview
PDF
Approximate_bayesian_computation.pdf

Download (428kB)

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).

Type: Article
Title: Approximate Bayesian computation.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1002803
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1002803
Language: English
Additional information: © 2013 Sunnåker et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. PMCID: PMC3547661
Keywords: Algorithms, Bayes Theorem, Quality Control
UCL classification: UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Biosciences (Division of) > Genetics, Evolution and Environment
URI: http://discovery.ucl.ac.uk/id/eprint/1371148
Downloads since deposit
0Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item