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dc.contributor.authorWu, Changye
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2019-04-16T08:40:08Z
dc.date.available2019-04-16T08:40:08Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18652
dc.language.isoenen
dc.subjectMarkov processen
dc.subjectbouncy particle sampleren
dc.subject.ddc621.3en
dc.titleGeneralized Bouncy Particle Sampleren
dc.typeDocument de travail / Working paper
dc.description.abstractenAs a special example of piecewise deterministic Markov process, bouncy particle sampler is a rejection-free, irreversible Markov chain Monte Carlo algorithm and can draw samples from target distribution efficiently. We generalize bouncy particle sampler in terms of its transition dynamics. In BPS, the transition dynamic at event time is deterministic, but in GBPS, it is random. With the help of this randomness, GBPS can overcome the reducibility problem in BPS without refreshement.en
dc.publisher.nameCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages28en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01968784en
dc.subject.ddclabelTraitement du signalen
dc.identifier.citationdate2017
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-03-26T13:16:45Z
hal.person.labIds60
hal.person.labIds60$$$2579


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