Show simple item record

dc.contributor.authorAndrieu, Christophe
dc.contributor.authorLee, Anthony
dc.contributor.authorVihola, Matti
dc.contributor.editorSisson, Scott A.
dc.contributor.editorFan, Yanan
dc.contributor.editorBeaumont, Mark
dc.date.accessioned2018-08-31T06:56:16Z
dc.date.available2019-08-11T21:35:29Z
dc.date.issued2018
dc.identifier.citationAndrieu, C., Lee, A., & Vihola, M. (2018). Theoretical and methodological aspects of MCMC computations with noisy likelihoods. In S. A. Sisson, Y. Fan, & M. Beaumont (Eds.), <i>Handbook of Approximate Bayesian Computation : Likelihood-Free Methods for Complex Model</i> (pp. 243-268). Chapman and Hall/CRC. Chapman & Hall/CRC Handbooks of Modern Statistical Methods.
dc.identifier.otherCONVID_28212480
dc.identifier.otherTUTKAID_78533
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/59401
dc.description.abstractApproximate Bayesian computation (ABC) [11, 42] is a popular method for Bayesian inference involving an intractable, or expensive to evaluate, likelihood function but where simulation from the model is easy. The method consists of defining an alternative likelihood function which is also in general intractable but naturally lends itself to pseudo-marginal computations [5], hence making the approach of practical interest. The aim of this chapter is to show the connections of ABC Markov chain Monte Carlo with pseudo-marginal algorithms, review their existing theoretical results, and discuss how these can inform practice and hopefully lead to fruitful methodological developments.fi
dc.format.extent662
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherChapman and Hall/CRC
dc.relation.ispartofHandbook of Approximate Bayesian Computation : Likelihood-Free Methods for Complex Model
dc.relation.ispartofseriesChapman & Hall/CRC Handbooks of Modern Statistical Methods
dc.rightsIn Copyright
dc.subject.otherBayesian computation
dc.subject.otherlikelihoods
dc.titleTheoretical and methodological aspects of MCMC computations with noisy likelihoods
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-201808163848
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/BookItem
dc.date.updated2018-08-16T12:15:10Z
dc.relation.isbn978-1-4398-8150-7
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.description.reviewstatuspeerReviewed
dc.format.pagerange243-268
dc.type.versionacceptedVersion
dc.rights.copyright© the Authors & Chapman and Hall/CRC, 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber274740
dc.subject.ysotodennäköisyyslaskenta
dc.subject.ysobayesilainen menetelmä
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p4746
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiatutkija, SAfi
jyx.fundingprogramAcademy Research Fellow, AoFen
dc.type.okmA3


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

In Copyright
Except where otherwise noted, this item's license is described as In Copyright