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dc.contributor.authorCasella, George
dc.contributor.authorMengersen, Kerrie
dc.contributor.authorRobert, Christian P.
dc.contributor.authorTitterington, Mike
dc.date.accessioned2011-07-18T13:33:15Z
dc.date.available2011-07-18T13:33:15Z
dc.date.issued2002
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6719
dc.descriptionLa version de travail attachée s'intitule "Perfect Slice Samplers for Mixtures of Distributions".en
dc.language.isoenen
dc.subjectCouplingen
dc.subjectGibbs samplingen
dc.subjectMarginalizationen
dc.subjectMonotonicityen
dc.subjectSlice samplingen
dc.subject.ddc519en
dc.titlePerfect samplers for mixtures of distributionsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe consider the construction of perfect samplers for posterior distributions associated with mixtures of exponential families and conjugate priors, starting with a perfect slice sampler in the spirit of Mira and co-workers. The methods rely on a marginalization akin to Rao–Blackwellization and illustrate the duality principle of Diebolt and Robert. A first approximation embeds the finite support distribution on the latent variables within a continuous support distribution that is easier to simulate by slice sampling, but we later demonstrate that the approximation can be very poor. We conclude by showing that an alternative perfect sampler based on a single backward chain can be constructed. This alternative can handle much larger sample sizes than the slice sampler first proposed.en
dc.relation.isversionofjnlnameJournal of the Royal Statistical Society. Series B, Statistical Methodology
dc.relation.isversionofjnlvol64en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2002
dc.relation.isversionofjnlpages777-790en
dc.relation.isversionofdoihttp://dx.doi.org/10.1111/1467-9868.00360en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherWileyen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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