Evidence and Bayes factor estimation for Gibbs random fields

Gibbs random fields play an important role in statistics. However they are complicated to work with due to an intractability of the likelihood function and there has been much work devoted to finding computational algorithms to allow Bayesian inference to be conducted for such so-called doubly intractable distributions. This paper extends this work and addresses the issue of estimating the evidence and Bayes factor for such models. The approach which we develop is shown to yield good performance.