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dc.contributor.authorVihola, Matti
dc.contributor.authorHelske, Jouni
dc.contributor.authorFranks, Jordan
dc.date.accessioned2020-09-08T08:59:59Z
dc.date.available2020-09-08T08:59:59Z
dc.date.issued2020
dc.identifier.citationVihola, M., Helske, J., & Franks, J. (2020). Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo. <i>Scandinavian Journal of Statistics</i>, <i>47</i>(4), 1339-1376. <a href="https://doi.org/10.1111/sjos.12492" target="_blank">https://doi.org/10.1111/sjos.12492</a>
dc.identifier.otherCONVID_41937280
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71682
dc.description.abstractWe consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context of Bayesian latent variable models, the MCMC typically operates on the hyperparameters, and the subsequent weighting may be based on IS or sequential Monte Carlo (SMC), but allows for multilevel techniques as well. The IS approach provides a natural alternative to delayed acceptance (DA) pseudo-marginal/particle MCMC, and has many advantages over DA, including a straightforward parallelisation and additional flexibility in MCMC implementation. We detail minimal conditions which ensure strong consistency of the suggested estimators, and provide central limit theorems with expressions for asymptotic variances. We demonstrate how our method can make use of SMC in the state space models context, using Laplace approximations and time-discretised diffusions. Our experimental results are promising and show that the IS type approach can provide substantial gains relative to an analogous DA scheme, and is often competitive even without parallelisation.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofseriesScandinavian Journal of Statistics
dc.rightsIn Copyright
dc.subject.otherMarkov chain Monte Carlo (MCMC)
dc.subject.otherBayesian analysis
dc.titleImportance sampling type estimators based on approximate marginal Markov chain Monte Carlo
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202009085789
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange1339-1376
dc.relation.issn0303-6898
dc.relation.numberinseries4
dc.relation.volume47
dc.type.versionacceptedVersion
dc.rights.copyright© Wiley-Blackwell, 2020
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber315619
dc.relation.grantnumber274740
dc.relation.grantnumber284513
dc.relation.grantnumber312605
dc.subject.ysobayesilainen menetelmä
dc.subject.ysotilastomenetelmät
dc.subject.ysoestimointi
dc.subject.ysoMarkovin ketjut
dc.subject.ysoMonte Carlo -menetelmät
dc.subject.ysootanta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p11349
jyx.subject.urihttp://www.yso.fi/onto/yso/p13075
jyx.subject.urihttp://www.yso.fi/onto/yso/p6361
jyx.subject.urihttp://www.yso.fi/onto/yso/p12939
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1111/sjos.12492
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
dc.relation.funderAcademy of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAkatemiatutkija, SAfi
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAcademy Research Fellow, AoFen
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundinginformationThe authors have been supported by the Academy of Finland grants 274740, 284513, 312605 and 315619.


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