Approximate Bayesian Computational methods
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.
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|Date of creation:||2012|
|Date of revision:|
|Publication status:||Published in Statistics and Computing, 2012, Vol. 22, no. 6. pp. 1167-1180.Length: 13 pages|
|Contact details of provider:|| Web page: http://www.dauphine.fr/en/welcome.html|
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