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dc.contributor.authorJasra, Ajay
dc.contributor.authorLaw, Kody
dc.contributor.authorSuciu, Carina
dc.date.accessioned2020-01-13T14:47:38Z
dc.date.available2017-12-28T07:32:11Z
dc.date.available2020-01-13T14:47:38Z
dc.date.issued2020-03-03
dc.date.submitted2019-06-10
dc.identifier.citationJasra, A., Law, K., & Suciu, C. (2020). Advanced Multilevel Monte Carlo Methods. International Statistical Review. doi:10.1111/insr.12365
dc.identifier.doi10.1111/insr.12365
dc.identifier.urihttp://hdl.handle.net/10754/626463
dc.description.abstractThis article reviews the application of some advanced Monte Carlo techniques in the context of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations, which can be biassed in some sense, for instance, by using the discretization of an associated probability law. The MLMC approach works with a hierarchy of biassed approximations, which become progressively more accurate and more expensive. Using a telescoping representation of the most accurate approximation, the method is able to reduce the computational cost for a given level of error versus i.i.d. sampling from this latter approximation. All of these ideas originated for cases where exact sampling from couples in the hierarchy is possible. This article considers the case where such exact sampling is not currently possible. We consider some Markov chain Monte Carlo and sequential Monte Carlo methods, which have been introduced in the literature, and we describe different strategies that facilitate the application of MLMC within these methods.
dc.description.sponsorshipA. J. and C. S. were supported under the KAUST Competitive Research Grants Program—Round 4 (CRG4) project, advanced multilevel sampling techniques for Bayesian inverse problems with applications to subsurface, ref: 2584. K. J. H. L. was supported by Oak RidgeNational Laboratory (ORNL) Directed Research and Development Seed funding and much ofthis work was performed while he was a staff member at ORNL. We thank the editor and two reviewers whose comments have greatly improved the paper.
dc.publisherWiley
dc.relation.urlhttps://onlinelibrary.wiley.com/doi/abs/10.1111/insr.12365
dc.rightsArchived with thanks to International Statistical Review
dc.subjectMultilevel Monte Carlo
dc.subjectMarkov chain Monte Carlo
dc.subjectSequential Monte Carlo
dc.subjectEnsemble Kalman filter
dc.subjectCoupling
dc.titleAdvanced Multilevel Monte Carlo Methods
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA
dc.identifier.journalInternational Statistical Review
dc.rights.embargodate2021-03-03
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Mathematics, University of Manchester, Manchester, M13 9PL, UK
dc.identifier.arxividarXiv:1704.07272
kaust.personJasra, Ajay
kaust.personSuciu, Carina
kaust.grant.numberCRG4
dc.date.accepted2020-02-04
refterms.dateFOA2018-06-14T05:32:34Z
kaust.acknowledged.supportUnitCompetitive Research
dc.date.posted2017-04-24


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