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dc.contributor.authorRobert, Christian P.*
dc.contributor.authorElvira, Víctor*
dc.contributor.authorTawn, Nick*
dc.contributor.authorWu, Changye*
dc.date.accessioned2019-03-25T15:06:04Z
dc.date.available2019-03-25T15:06:04Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18568
dc.language.isoenen
dc.subjectMarkov chain
dc.subjectMonte Carlo algorithms
dc.subject.ddc621.3en
dc.titleAccelerating MCMC algorithms
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenMarkov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations that grows with the dimension of the problem and with the complexity of the data behind it. Several techniques are available toward accelerating the convergence of these Monte Carlo algorithms, either at the exploration level (as in tempering, Hamiltonian Monte Carlo and partly deterministic methods) or at the exploitation level (with Rao–Blackwellization and scalable methods).
dc.relation.isversionofjnlnameWiley Interdisciplinary Reviews: Computational Statistics
dc.relation.isversionofjnlvol10
dc.relation.isversionofjnlissue5
dc.relation.isversionofjnldate2018
dc.relation.isversionofjnlpages1-22
dc.relation.isversionofdoi10.1002/wics.1435
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01961128
dc.subject.ddclabelTraitement du signalen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewednon
dc.date.updated2019-12-18T14:42:47Z
hal.person.labIds60*
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