Approximate Bayesian Computational methods

Marin, Jean-Michel; Pudlo, Pierre; Robert, Christian P.; Ryder, Robin J.

Marin, Jean-Michel; Pudlo, Pierre; Robert, Christian P.; Ryder, Robin J. (2012), Approximate Bayesian Computational methods, Statistics and Computing, 22, 6, p. 1167-1180. http://dx.doi.org/10.1007/s11222-011-9288-2

Type

Article accepté pour publication ou publié

Lien vers un document non conservé dans cette base

https://arxiv.org/abs/1101.0955

Date

2012

Nom de la revue

Statistics and Computing

Volume

Numéro

Éditeur

Springer

Pages

1167-1180

Résumé (EN)

Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.

Mots-clés

likelihood-free methods; Bayesian statistics; ABC Methodology; DIYABC; Bayesian model chance

JEL

C15 - Statistical Simulation Methods: General
C11 - Bayesian Analysis: General