Approximate Bayesian computation.

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,...

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Autores principales:	Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander, Matthieu Foll, Christophe Dessimoz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9376965912b5427199a2a3385b7ddec0
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spelling	oai:doaj.org-article:9376965912b5427199a2a3385b7ddec02021-11-18T05:52:32ZApproximate Bayesian computation.1553-734X1553-735810.1371/journal.pcbi.1002803https://doaj.org/article/9376965912b5427199a2a3385b7ddec02013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23341757/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Approximate 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).Mikael SunnåkerAlberto Giovanni BusettoElina NumminenJukka CoranderMatthieu FollChristophe DessimozPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 1, p e1002803 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Mikael Sunnåker Alberto Giovanni Busetto Elina Numminen Jukka Corander Matthieu Foll Christophe Dessimoz Approximate Bayesian computation.
description	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).
format	article
author	Mikael Sunnåker Alberto Giovanni Busetto Elina Numminen Jukka Corander Matthieu Foll Christophe Dessimoz
author_facet	Mikael Sunnåker Alberto Giovanni Busetto Elina Numminen Jukka Corander Matthieu Foll Christophe Dessimoz
author_sort	Mikael Sunnåker
title	Approximate Bayesian computation.
title_short	Approximate Bayesian computation.
title_full	Approximate Bayesian computation.
title_fullStr	Approximate Bayesian computation.
title_full_unstemmed	Approximate Bayesian computation.
title_sort	approximate bayesian computation.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/9376965912b5427199a2a3385b7ddec0
work_keys_str_mv	AT mikaelsunnaker approximatebayesiancomputation AT albertogiovannibusetto approximatebayesiancomputation AT elinanumminen approximatebayesiancomputation AT jukkacorander approximatebayesiancomputation AT matthieufoll approximatebayesiancomputation AT christophedessimoz approximatebayesiancomputation
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Approximate Bayesian computation.

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