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dc.contributor.authorSchäfer, Christian*
dc.contributor.authorIacobucci, Alessandra*
dc.contributor.authorRobert, Christian P.*
dc.contributor.authorMengersen, Kerrie*
dc.contributor.authorChopin, Nicolas*
dc.contributor.authorRyder, Robin J.*
dc.contributor.authorMarin, Jean-Michel*
dc.date.accessioned2010-07-22T14:14:36Z
dc.date.available2010-07-22T14:14:36Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4640
dc.language.isoenen
dc.subjectAttritionen
dc.subjectdegeneracyen
dc.subjectevidenceen
dc.subjectimportance samplingen
dc.subjectMarginal likelihooden
dc.subjectMarkov chain Monte Carloen
dc.subjectmixtures of distributionsen
dc.subjectsimulationen
dc.subjectsequential samplingen
dc.subjectparticle filteren
dc.subject.ddc519en
dc.subject.classificationjelC15en
dc.titleOn Particle Learningen
dc.typeDocument de travail / Working paper
dc.description.abstractenThis document is the aggregation of six discussions of Lopes et al. (2010) that we submitted to the proceedings of the Ninth Valencia Meeting, held in Benidorm, Spain, on June 3-8, 2010, in conjunction with Hedibert Lopes' talk at this meeting. The main point in those discussions is the potential for degeneracy in the particle learning methodology, related with the exponential forgetting of the past simulations. We illustrate in particular the resulting difficulties in the case of mixtures.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages19en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00494357/fr/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
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