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dc.contributor.authorCappé, Olivier
dc.contributor.authorDouc, Randal
dc.contributor.authorGuillin, Arnaud
dc.contributor.authorMarin, Jean-Michel
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2010-02-15T15:31:55Z
dc.date.available2010-02-15T15:31:55Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/3465
dc.language.isoenen
dc.subjectImportance samplingen
dc.subjectAdaptive Monte Carloen
dc.subjectMixture modelen
dc.subjectEntropyen
dc.subjectKullback-Leibler divergenceen
dc.subjectEM algorithmen
dc.subjectPopulation Monte Carloen
dc.subject.ddc519en
dc.titleAdaptive Importance Sampling in General Mixture Classesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.en
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol18en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2008-12
dc.relation.isversionofjnlpages447-459en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s11222-008-9059-xen
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00180669/en/en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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