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Adaptive approximate Bayesian computation

dc.contributor.authorRobert, Christian P.
dc.contributor.authorMarin, Jean-Michel
dc.contributor.authorCornuet, Jean-Marie
dc.contributor.authorBeaumont, Mark A.
dc.date.accessioned2010-02-02T14:46:44Z
dc.date.available2010-02-02T14:46:44Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/3221
dc.descriptionLe lien ci-dessous donne accès à la version working paper, intitulée "Adaptivity for ABC algorithms: the ABC-PMC scheme"
dc.language.isoenen
dc.subjectImportance samplingen
dc.subjectMarkov chain Monte Carloen
dc.subjectPartial rejection controlen
dc.subjectSequential Monte Carloen
dc.subject.ddc519en
dc.titleAdaptive approximate Bayesian computationen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherUniversité Montpellier 2, Montpellier;France
dc.contributor.editoruniversityotherSchool of Biological Sciences, University of Reading,Whiteknights;Royaume-Uni
dc.contributor.editoruniversityotherDepartment of Epidemiology and Public Health, Imperial College, London;Royaume-Uni
dc.description.abstractenSequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.’s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappé et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.en
dc.relation.isversionofjnlnameBiometrika
dc.relation.isversionofjnlvol96en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2009-10
dc.relation.isversionofjnlpages983-990en
dc.relation.isversionofdoihttp://dx.doi.org/10.1093/biomet/asp052en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00280461/en/
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherOxford University Pressen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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