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Approximate Bayesian Computational methods

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
External document link
https://arxiv.org/abs/1101.0955
Date
2012
Journal name
Statistics and Computing
Volume
22
Number
6
Publisher
Springer
Pages
1167-1180
Publication identifier
http://dx.doi.org/10.1007/s11222-011-9288-2
Metadata
Show full item record
Author(s)
Marin, Jean-Michel cc

Pudlo, Pierre

Robert, Christian P.

Ryder, Robin J.
Abstract (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.
Subjects / Keywords
likelihood-free methods; Bayesian statistics; ABC Methodology; DIYABC; Bayesian model chance
JEL
C15 - Statistical Simulation Methods: General
C11 - Bayesian Analysis: General

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