Approximate Bayesian computational methods

Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degr...

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Bibliographic Details
Published in:Statistics and computing Vol. 22; no. 6; pp. 1167 - 1180
Main Authors: Marin, Jean-Michel, Pudlo, Pierre, Robert, Christian P., Ryder, Robin J.
Format: Journal Article
Language:English
Published: Boston Springer US 01.11.2012
Springer Verlag (Germany)
Subjects:
Artificial Intelligence
Mathematics
Mathematics and Statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Bayesian model choice
DIYABC
Bayesian statistics
ABC methodology
Likelihood-free methods
ISSN:0960-3174, 1573-1375
Online Access:Get full text
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Summary:Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable 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 brought on the original ABC algorithm in recent years.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-011-9288-2