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dc.contributor.authorRaynal, Louis*
hal.structure.identifier
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
*
hal.structure.identifier
dc.contributor.authorPudlo, Pierre*
hal.structure.identifier
dc.contributor.authorRibatet, Mathieu
HAL ID: 3247
ORCID: 0000-0003-0231-6001
*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRobert, Christian P.*
hal.structure.identifier
dc.contributor.authorEstoup, Arnaud
HAL ID: 745567
ORCID: 0000-0002-4357-6144
*
dc.date.accessioned2019-03-25T14:28:40Z
dc.date.available2019-03-25T14:28:40Z
dc.date.issued2019
dc.identifier.issn1367-4803
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18566
dc.descriptionArticle
dc.language.isoenen
dc.subjectApproximate Bayesian computation
dc.subjectBayesian inference
dc.subjectlikelihood-free methods
dc.subjectparameter inference
dc.subjectrandom forests
dc.subject.ddc621.3en
dc.titleABC random forests for Bayesian parameter inference
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenApproximate Bayesian computation (ABC) has grown into a standard methodology that manages Bayesian inference for models associated with intractable likelihood functions. Most ABC implementations require the preliminary selection of a vector of informative statistics summarizing raw data. Furthermore, in almost all existing implementations, the tolerance level that separates acceptance from rejection of simulated parameter values needs to be calibrated. We propose to conduct likelihood-free Bayesian inferences about parameters with no prior selection of the relevant components of the summary statistics and bypassing the derivation of the associated tolerance level. The approach relies on the random forest methodology of Breiman (2001) applied in a (non parametric) regression setting. We advocate the derivation of a new random forest for each component of the parameter vector of interest. When compared with earlier ABC solutions, this method offers significant gains in terms of robustness to the choice of the summary statistics, does not depend on any type of tolerance level, and is a good trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. We illustrate the performance of our methodological proposal and compare it with earlier ABC methods on a Normal toy example and a population genetics example dealing with human population evolution. All methods designed here have been incorporated in the R package abcrf (version 1.7) available on CRAN.
dc.relation.isversionofjnlnameBioinformatics
dc.relation.isversionofjnlvol35
dc.relation.isversionofjnlissue10
dc.relation.isversionofjnldate2019
dc.relation.isversionofjnlpages1720-1728
dc.relation.isversionofdoi10.1093/bioinformatics/bty867
dc.relation.isversionofjnlpublisherOxford University Press
dc.subject.ddclabelTraitement du signalen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
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dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2020-03-06T13:44:49Z
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