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Approximate Bayesian Computation: A Survey on Recent Results

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
Publication identifier
10.1007/978-3-319-33507-0_7
Metadata
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Author(s)
Robert, Christian P.
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

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