Multilevel Monte Carlo in Approximate Bayesian Computation

Handle URI:
http://hdl.handle.net/10754/626522
Title:
Multilevel Monte Carlo in Approximate Bayesian Computation
Authors:
Jasra, Ajay; Jo, Seongil; Nott, David; Shoemaker, Christine; Tempone, Raul ( 0000-0003-1967-4446 )
Abstract:
In 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Center for Uncertainty Quantification in Computational Science & Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA.
Publisher:
arXiv
Issue Date:
13-Feb-2017
ARXIV:
arXiv:1702.03628
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1702.03628v1; http://arxiv.org/pdf/1702.03628v1
Appears in Collections:
Other/General Submission; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJasra, Ajayen
dc.contributor.authorJo, Seongilen
dc.contributor.authorNott, Daviden
dc.contributor.authorShoemaker, Christineen
dc.contributor.authorTempone, Raulen
dc.date.accessioned2017-12-28T07:32:14Z-
dc.date.available2017-12-28T07:32:14Z-
dc.date.issued2017-02-13en
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.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1702.03628v1en
dc.relation.urlhttp://arxiv.org/pdf/1702.03628v1en
dc.rightsArchived with thanks to arXiven
dc.titleMultilevel Monte Carlo in Approximate Bayesian Computationen
dc.typePreprinten
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentCenter for Uncertainty Quantification in Computational Science & Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-6900, KSA.en
dc.eprint.versionPre-printen
dc.contributor.institutionDepartment of Statistics & Applied Probability & Operations Research Cluster, National University of Singapore, Singapore, 117546, SG.en
dc.contributor.institutionDepartment of Civil & Environmental Engineering & Operations Research Cluster, National University of Singapore, Singapore, 119260, SG.en
dc.identifier.arxividarXiv:1702.03628en
kaust.authorTempone, Raulen
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