  We consider the problem of estimating the interacting neighborhood of a
Markov Random Field model with finite support and homogeneous pairwise
interactions based on relative positions of a two-dimensional lattice. Using a
Bayesian framework, we propose a Reversible Jump Monte Carlo Markov Chain
algorithm that jumps across subsets of a maximal range neighborhood, allowing
us to perform model selection based on a marginal pseudoposterior distribution
of models. To show the strength of our proposed methodology we perform a
simulation study and apply it to a real dataset from a discrete texture image
analysis.
