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Approximate Bayesian computation, an introduction

Robert, Christian P. (2021), Approximate Bayesian computation, an introduction, in Jean-Baptiste Marquette, Didier Fraix‐Burnet , Stéphane Girard and Julyan Arbel, Statistics for Astrophysics, EDP Sciences : Les Ulis, p. 77-112. 10.1051/978-2-7598-2275-1.c008

Type
Chapitre d'ouvrage
Date
2021
Book title
Statistics for Astrophysics
Book author
Jean-Baptiste Marquette, Didier Fraix‐Burnet , Stéphane Girard and Julyan Arbel
Publisher
EDP Sciences
Published in
Les Ulis
ISBN
9782759817290
Number of pages
140
Pages
77-112
Publication identifier
Metadata
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Author(s)
Robert, Christian P.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
Approximate Bayesian Computation (ABC) methods have become a “mainstream” statistical technique in the past decade, following the realisation that they were a form of non-parametric inference, and connected as well with the econometric technique of indirect inference. In this survey of ABC methods, we focus on the basics of ABC and cover some of the recent literature, following our earlier survey in Marin et al.(2011). Given the recent paradigm shift in the perception and practice of ABC model choice, we insist on this aspect of ABC techniques, including in addition some convergence results.