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dc.contributor.authorJasra, Ajay
dc.contributor.authorLaw, Kody J. H.
dc.contributor.authorXu, Yaxian
dc.date.accessioned2021-02-10T08:02:40Z
dc.date.available2020-01-13T12:59:05Z
dc.date.available2021-02-10T08:02:40Z
dc.date.issued2021
dc.date.submitted2020-10
dc.identifier.citationJasra, A., Law, K. J. H., & Xu, Y. (2021). Markov chain simulation for multilevel Monte Carlo. Foundations of Data Science, 0–0. doi:10.3934/fods.2021004
dc.identifier.issn2639-8001
dc.identifier.doi10.3934/fods.2021004
dc.identifier.urihttp://hdl.handle.net/10754/661004
dc.description.abstractThis paper considers a new approach to using Markov chain Monte Carlo (MCMC) in contexts where one may adopt multilevel (ML) Monte Carlo. The underlying problem is to approximate expectations w.r.t. an underlying probability measure that is associated to a continuum problem, such as a continuous-time stochastic process. It is then assumed that the associated probability measure can only be used (e.g. sampled) under a discretized approximation. In such scenarios, it is known that to achieve a target error, the computational effort can be reduced when using MLMC relative to i.i.d. sampling from the most accurate discretized probability. The ideas rely upon introducing hierarchies of the discretizations where less accurate approximations cost less to compute, and using an appropriate collapsing sum expression for the target expectation. If a suitable coupling of the probability measures in the hierarchy is achieved, then a reduction in cost is possible. This article focused on the case where exact sampling from such coupling is not possible. We show that one can construct suitably coupled MCMC kernels when given only access to MCMC kernels which are invariant with respect to each discretized probability measure. We prove, under verifiable assumptions, that this coupled MCMC approach in a ML context can reduce the cost to achieve a given error, relative to exact sampling. Our approach is illustrated on a numerical example.
dc.description.sponsorshipAJ was supported by an AcRF tier 2 grant: R-155-000-161-112. AJ is affiliated with the Risk Management Institute, the Center for Quantitative Finance and the OR & Analytics cluster at NUS. AJ was supported by a KAUST CRG4 grant ref: 2584. KJHL was supported by the University of Manchester School of Mathematics.
dc.language.isoen
dc.publisherAmerican Institute of Mathematical Sciences (AIMS)
dc.relation.urlhttps://www.aimsciences.org/article/doi/10.3934/fods.2021004
dc.rightsThis is a pre-copy-editing, author-produced PDF of an article accepted for publication in Foundations of Data Science following peer review. The definitive publisher-authenticated version is available online at: http://doi.org/10.3934/fods.2021004
dc.titleMarkov chain simulation for multilevel Monte Carlo
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalFoundations of Data Science
dc.rights.embargodate2022-02-10
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Mathematics, University of Manchester, Manchester, M13 9PL, UK
dc.contributor.institutionDepartment of Statistics and Applied Probability, National University of Singapore, Singapore, 117546, SG
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.pages0-0
pubs.publication-statusSubmitted
dc.identifier.arxivid1806.09754
kaust.personJasra, Ajay
kaust.grant.numberCRG4 grant ref: 2584
dc.date.accepted2021-01
dc.versionv1
refterms.dateFOA2020-01-13T12:59:06Z
dc.date.posted2018-06-26


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