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dc.contributor.authorRobert, Christian P.
dc.contributor.authorCeleux, Gilles
dc.contributor.authorKamary, Kaniav
dc.contributor.authorMalsiner-Walli, Gertraud
dc.contributor.authorMarin, Jean-Michel
dc.date.accessioned2019-03-26T12:55:57Z
dc.date.available2019-03-26T12:55:57Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18582
dc.language.isoenen
dc.subjectBayesian Inferenceen
dc.subjectMixture Modelsen
dc.subject.ddc515en
dc.titleComputational Solutions for Bayesian Inference in Mixture Modelsen
dc.typeChapitre d'ouvrage
dc.description.abstractenThis chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the robustness of the Gibbs sampler in high dimensional Gaussian settings. This is a chapter prepared for the forthcoming 'Handbook of Mixture Analysis'.en
dc.identifier.citationpages24en
dc.relation.ispartoftitleHandbook of Mixture Analysisen
dc.relation.ispartofeditorSylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert
dc.relation.ispartofpublnameCRC Press, Taylor & Francisen
dc.relation.ispartofdate2019-01
dc.relation.ispartofpages498en
dc.subject.ddclabelAnalyseen
dc.relation.ispartofisbn9781498763813en
dc.relation.forthcomingnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-03-26T12:51:35Z
hal.person.labIds60
hal.person.labIds34587
hal.person.labIds60
hal.person.labIds420887
hal.person.labIds631


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