Bayesian Analysis

ABC likelihood-free methods for model choice in Gibbs random fields

Aude Grelaud, Jean-Michel Marin, Christian P. Robert, François Rodolphe, and Jean-François Taly

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Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods is hampered by the unavailability of the normalising constant in the Gibbs likelihood. In particular, from a Bayesian perspective, the computation of the posterior probabilities of the models under competition requires special likelihood-free simulation techniques like the Approximate Bayesian Computation (ABC) algorithm that is intensively used in population genetics. We show in this paper how to implement an ABC algorithm geared towards model choice in the general setting of Gibbs random fields, demonstrating in particular that there exists a sufficient statistic across models. The accuracy of the approximation to the posterior probabilities can be further improved by importance sampling on the distribution of the models. The practical aspects of the method are detailed through two applications, the test of an iid Bernoulli model versus a first-order Markov chain, and the choice of a folding structure for two proteins.

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Bayesian Anal., Volume 4, Number 2 (2009), 317-335.

First available in Project Euclid: 22 June 2012

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Approximate Bayesian Computation model choice Gibbs Random Fields Bayes factor protein folding


Grelaud, Aude; Robert, Christian P.; Marin, Jean-Michel; Rodolphe, François; Taly, Jean-François. ABC likelihood-free methods for model choice in Gibbs random fields. Bayesian Anal. 4 (2009), no. 2, 317--335. doi:10.1214/09-BA412.

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