ABC methods for model choice in Gibbs random fields |
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| Authors: | Aude Grelaud Christian P. Robert Jean-Michel Marin |
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| Affiliation: | 1. INRA, unité MIG, domaine du Vilvert, 78350 Jouy-en-Josas, France;2. CEREMADE, Université Paris Dauphine, place du Maréchal-de-Lattre-de-Tassigny, 75775 Paris cedex 16, France;3. CREST-INSEE, Timbre J340, 3, avenue Pierre-Larousse, 92240 Malakoff, France;4. Institut de mathématiques et de modélisation de Montpellier, Université Montpellier 2, 34095 Montpellier cedex, France |
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| Abstract: | We consider the problem of model selection within the class of Gibbs random fields. In a Bayesian framework, this choice relies on the evaluation of the posterior probabilities of all models. We define an extended parameter setting, including the model index and show the existence of a corresponding sufficient statistic made of the conjunction of the sufficient statistics of all models. We use this statistic to derive an ABC algorithm. To cite this article: A. Grelaud et al., C. R. Acad. Sci. Paris, Ser. I 347 (2009). |
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