KAUST DepartmentApplied Mathematics and Computational Science Program
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Stochastic Numerics Research Group
KAUST Grant Number2584
Preprint Posting Date2017-02-13
Online Publication Date2019-01-31
Print Publication Date2019-05-04
Permanent link to this recordhttp://hdl.handle.net/10754/626522
MetadataShow full item record
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.
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.
SponsorsAJ, 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.
PublisherInforma UK Limited