  We propose prior distributions for all parts of the specification of a Markov
mesh model. In the formulation we define priors for the sequential
neighborhood, for the parametric form of the conditional distributions and for
the parameter values. By simulating from the resulting posterior distribution
when conditioning on an observed scene, we thereby obtain an automatic model
selection procedure for Markov mesh models. To sample from such a posterior
distribution, we construct a reversible jump Markov chain Monte Carlo algorithm
(RJMCMC). We demonstrate the usefulness of our prior formulation and the
limitations of our RJMCMC algorithm in two examples.
