  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.
