Multilevel Monte Carlo in approximate Bayesian computation
Type
Article
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Center for Uncertainty Quantification in Computational Science and Engineering (SRI-UQ)
KAUST Grant Number
2584Date
2019-02-01Permanent link to this record
http://hdl.handle.net/10754/626522Metadata
Show full item recordAbstract
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.Citation
Jasra 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.Sponsors
AJ, 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.Publisher
Informa UK LimitedISSN
0736-29941532-9356