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Coordinate sampler: a non-reversible Gibbs-like MCMC sampler

hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorWu, Changye*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRobert, Christian P.*
dc.date.accessioned2019-04-16T08:08:02Z
dc.date.available2019-04-16T08:08:02Z
dc.date.issued2020
dc.identifier.issn0960-3174
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18651
dc.language.isoenen
dc.subjectMarkov chain Monte Carlo
dc.subjectPiecewise deterministic Markovprocesses
dc.subjectZigzag sampling
dc.subjectGibbs sampling
dc.subject.ddc621.3en
dc.titleCoordinate sampler: a non-reversible Gibbs-like MCMC sampler
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe derive a novel non-reversible, continuous-time Markov chain Monte Carlo sampler, called Coordinate Sampler, based on a piecewise deterministic Markov process, which is a variant of the Zigzag sampler of Bierkens et al. (Ann Stat 47(3):1288–1320, 2019). In addition to providing a theoretical validation for this new simulation algorithm, we show that the Markov chain it induces exhibits geometrical ergodicity convergence, for distributions whose tails decay at least as fast as an exponential distribution and at most as fast as a Gaussian distribution. Several numerical examples highlight that our coordinate sampler is more efficient than the Zigzag sampler, in terms of effective sample size.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol30
dc.relation.isversionofjnldate2020
dc.relation.isversionofjnlpages721–730
dc.relation.isversionofdoi10.1007/s11222-019-09913-w
dc.relation.isversionofjnlpublisherSpringer
dc.subject.ddclabelTraitement du signalen
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dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2020-05-12T11:13:21Z
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