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| HAL: hal-00567240, version 1 |
| arXiv: 1101.0955 |
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| Statistics and Computing 22, 6 (2012) 1167-1180 |
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| Approximate Bayesian Computational methods |
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| Jean-michel Marin 1Pierre Pudlo 1 |
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| (2012) |
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| 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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| 1: | Institut de Mathématiques et de Modélisation de Montpellier (I3M) |
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CNRS : UMR5149 – Université Montpellier II - Sciences et techniques |
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| 2: | CEntre de REcherches en MAthématiques de la DEcision (CEREMADE) |
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CNRS : UMR7534 – Université Paris IX - Paris Dauphine |
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| 3: | Centre de Recherche en Économie et Statistique (CREST) |
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INSEE – École Nationale de la Statistique et de l'Administration Économique |
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| 4: | Institut Universitaire de France (IUF) |
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Ministère de l'Enseignement Supérieur et de la Recherche Scientifique |
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| Subject | : | Statistics/Computation |
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| Fulltext link: |
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| http://fr.arXiv.org/abs/1101.0955 |
| hal-00567240, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00567240 | |
| oai:hal.archives-ouvertes.fr:hal-00567240 | |
| From: Christian Robert | |
| Submitted on: Saturday, 19 February 2011 08:45:37 | |
| Updated on: Friday, 15 March 2013 16:31:34 | |
