  Gibbs random fields play an important role in statistics, for example the
autologistic model is commonly used to model the spatial distribution of binary
variables defined on a lattice. However they are complicated to work with due
to an intractability of the likelihood function. It is therefore natural to
consider tractable approximations to the likelihood function. Composite
likelihoods offer a principled approach to constructing such approximation. The
contribution of this paper is to examine the performance of a collection of
composite likelihood approximations in the context of Bayesian inference.
