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dc.contributor.authorJasra, Ajay
dc.contributor.authorLaw, Kody
dc.contributor.authorSuciu, Carina
dc.date.accessioned2017-12-28T07:32:11Z
dc.date.available2017-12-28T07:32:11Z
dc.date.issued2017-04-24
dc.identifier.urihttp://hdl.handle.net/10754/626463
dc.description.abstractThis article reviews the application of advanced Monte Carlo techniques in the context of Multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations which can be biased in some sense, for instance, by using the discretization of a associated probability law. The MLMC approach works with a hierarchy of biased 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 Markov chain Monte Carlo and sequential Monte Carlo methods which have been introduced in the literature and we describe different strategies which facilitate the application of MLMC within these methods.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1704.07272v1
dc.relation.urlhttp://arxiv.org/pdf/1704.07272v1
dc.rightsArchived with thanks to arXiv
dc.titleAdvanced Multilevel Monte Carlo Methods
dc.typePreprint
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science & Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Statistics & Applied Probability, National University of Singapore, Singapore, 117546, SG
dc.contributor.institutionComputer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, 37934 TN
dc.identifier.arxividarXiv:1704.07272
kaust.personSuciu, Carina
refterms.dateFOA2018-06-14T05:32:34Z


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