ABC methods for model choice in Gibbs random fields

摘要

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).