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dc.contributor.authorSchäfer, Christian
dc.date.accessioned2011-02-28T11:13:21Z
dc.date.available2011-02-28T11:13:21Z
dc.date.issued2011-02
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/5718
dc.language.isoenen
dc.subjectMultivariate binary dataen
dc.subjectBinary proposal distributionsen
dc.subjectAdaptive Monte Carloen
dc.subjectBinary parametric familiesen
dc.subject.ddc519en
dc.subject.classificationjelC15en
dc.titleParametric families on large binary spacesen
dc.typeDocument de travail / Working paper
dc.contributor.editoruniversityotherCentre de Recherche en Économie et Statistique (CREST) INSEE – École Nationale de la Statistique et de l'Administration Économique;France
dc.description.abstractenIn the context of adaptive Monte Carlo algorithms, we cannot directly generate independent samples from the distribution of interest but use a proxy which we need to be close to the target. Generally, such a proxy distribution is a parametric family on the sampling spaces of the target distribution. For continuous sampling problems in high dimensions, we often use the multivariate normal distribution as a proxy for we can easily parametrise it by its moments and quickly sample from it. Our objective is to construct similarly flexible parametric families on binary sampling spaces too large for exhaustive enumeration. The binary sampling problem is more difficult than its continuous counterpart since the choice of a suitable proxy distribution is not obvious.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages13en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00507420/fr/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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