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dc.contributor.authorJasra, Ajay
dc.contributor.authorJo, Seongil
dc.contributor.authorNott, David
dc.contributor.authorShoemaker, Christine
dc.contributor.authorTempone, Raul
dc.date.accessioned2019-03-17T13:03:39Z
dc.date.available2017-12-28T07:32:14Z
dc.date.available2019-03-17T13:03:39Z
dc.date.issued2019-02-01
dc.identifier.citationJasra A, Jo S, Nott D, Shoemaker C, Tempone R (2019) Multilevel Monte Carlo in approximate Bayesian computation. Stochastic Analysis and Applications: 1–15. Available: http://dx.doi.org/10.1080/07362994.2019.1566006.
dc.identifier.issn0736-2994
dc.identifier.issn1532-9356
dc.identifier.doi10.1080/07362994.2019.1566006
dc.identifier.urihttp://hdl.handle.net/10754/626522
dc.description.abstractIn the following article, we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.
dc.description.sponsorshipAJ, SJ, DN, and CS were all supported by grant number R-069-000-074-646, Operations research cluster funding, NUS. AJ and RT were additionally supported by KAUST CRG4 Award Ref: 2584.
dc.publisherInforma UK Limited
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/07362994.2019.1566006
dc.rightsArchived with thanks to Stochastic Analysis and Applications
dc.subjectApproximate Bayesian computation
dc.subjectmultilevel Monte Carlo
dc.subjectsequential Monte Carlo
dc.titleMultilevel Monte Carlo in approximate Bayesian computation
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
dc.identifier.journalStochastic Analysis and Applications
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Statistics and Applied Probability and Operations Research Cluster, National University of Singapore, Singapore, Singapore;
dc.contributor.institutionDepartment of Statistics, Chonbuk National University, Jeonju, Republic of Korea;
dc.contributor.institutionDepartment of Civil and Environmental Engineering and Operations Research Cluster, National University of Singapore, Singapore, Singapore;
dc.identifier.arxividarXiv:1702.03628
kaust.personTempone, Raul
kaust.grant.number2584
refterms.dateFOA2018-06-13T17:21:21Z


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