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dc.contributor.authorSingh, Sumeetpalen
dc.contributor.authorLindsten, Fen
dc.contributor.authorMoulines, Een
dc.date.accessioned2017-09-27T14:36:35Z
dc.date.available2017-09-27T14:36:35Z
dc.date.issued2017-12-01en
dc.identifier.issn0006-3444
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/267424
dc.description.abstractSampling from the posterior probability distribution of the latent states of a hidden Markov model is non-trivial even in the context of Markov chain Monte Carlo. To address this Andrieu et al. (2010) proposed a way of using a particle filter to construct a Markov kernel that leaves his posterior distribution invariant. Recent theoretical results establish the uniform ergodicity of this Markov kernel and show that the mixing rate does not deteriorate provided the number of particles grows at least linearly with the number of latent states. However, this gives rise to a cost per application of the kernel that is quadratic in the number of latent states, which can be prohibitive for long observation sequences. Using blocking strategies, we devise samplers that have a stable mixing rate for a cost per iteration that is linear in the number of latent states and which are easily parallelizable.
dc.description.sponsorshipThe authors thank the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the programme Monte Carlo Inference for Complex Statistical Models when work on this paper was undertaken. This work was supported by the Engineering and Physical Sciences Research Council [grant numbers EP/K020153/1, EP/K032208/1] and the Swedish Research Council [contract number 2016-04278].
dc.language.isoenen
dc.publisherOxford University Press
dc.rightsAttribution 4.0 International
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectParticle Gibbs samplingen
dc.subjectHidden Markov modelen
dc.subjectMarkov chain Monte Carloen
dc.subjectparticle filteren
dc.titleBlocking strategies and stability of particle Gibbs samplersen
dc.typeArticle
prism.endingPage969
prism.issueIdentifier4en
prism.publicationDate2017en
prism.publicationNameBiometrikaen
prism.startingPage953
prism.volume104en
dc.identifier.doi10.17863/CAM.12029
dcterms.dateAccepted2017-07-14en
rioxxterms.versionofrecord10.1093/biomet/asx051en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-12-01en
dc.identifier.eissn1464-3510
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/K020153/1)
cam.issuedOnline2017-10-16en
cam.orpheus.successThu Jan 30 12:59:57 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International