Approximate Bayesian Computation: A Survey on Recent Results

Robert, Christian P.

Robert, Christian P. (2016), Approximate Bayesian Computation: A Survey on Recent Results, in Cools, Ronald; Nuyens, Dirk, Monte Carlo and Quasi-Monte Carlo Methods. MCQMC, Leuven, Belgium, April 2014, Springer International Publishing : Heidelberg, p. 185-205. 10.1007/978-3-319-33507-0_7

Type

Communication / Conférence

Date

2016

Conference title

Monte Carlo and Quasi-Monte Carlo Methods

Conference date

2014-04

Conference city

Leuven

Conference country

Belgium

Book title

Monte Carlo and Quasi-Monte Carlo Methods. MCQMC, Leuven, Belgium, April 2014

Book author

Cools, Ronald; Nuyens, Dirk

Publisher

Springer International Publishing

Published in

Heidelberg

ISBN

978-3-319-33505-6

Pages

185-205

Abstract (EN)

Approximate Bayesian Computation (ABC) methods have become a “mainstream” statistical technique in the past decade, following the realisation by statisticians that they are a special type of non-parametric inference. In this survey of ABC methods, we focus on the recent literature, building on the previous survey of Marin et al. Stat Comput 21(2):279–291, 2011, [39]. Given the importance of model choice in the applications of ABC, and the associated difficulties in its implementation, we also give emphasis to this aspect of ABC techniques.

Subjects / Keywords

Approximate Bayesian computation; Likelihood-free methods; Bayesian model choice; Sufficiency; Monte Carlo methods; Summary statistics