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hal.structure.identifier
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
hal.structure.identifier
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2018-01-12T18:37:50Z
dc.date.available2018-01-12T18:37:50Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17318
dc.language.isoenen
dc.subjectimportance samplingen
dc.subjectbridge samplingen
dc.subjectharmonic meanen
dc.subjectChib's estimatoren
dc.subjectBayesian model choiceen
dc.subject.ddc519en
dc.titleImportance sampling methods for Bayesian discrimination between embedded modelsen
dc.typeChapitre d'ouvrage
dc.description.abstractenThis paper surveys some well-established approaches on the approximation of Bayes factors used in Bayesian model choice, mostly as covered in Chen et al. (2000). Our focus here is on methods that are based on importance sampling strategies rather than variable dimension techniques like reversible jump MCMC, including: crude Monte Carlo, maximum likelihood based importance sampling, bridge and harmonic mean sampling, as well as Chib's method based on the exploitation of a functional equality. We demonstrate in this survey how these different methods can be efficiently implemented for testing the significance of a predictive variable in a probit model. Finally, we compare their performances on a real dataset.en
dc.identifier.citationpages513-527en
dc.relation.ispartoftitleFrontiers of Statistical Decision Making and Bayesian Analysisen
dc.relation.ispartofeditorChen, M.H.
dc.relation.ispartofeditorMüller, P.
dc.relation.ispartofeditorSun, D.
dc.relation.ispartofeditorYe, K.
dc.relation.ispartofeditorDey, D.
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityBerlin Heidelbergen
dc.relation.ispartofdate2010
dc.relation.ispartofurl10.1007/978-1-4419-6944-6en
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00424475en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.ispartofisbn978-1-4419-6943-9en
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-1-4419-6944-6_14en
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2018-01-11T15:41:17Z
hal.author.functionaut
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