Bayesian smoothing in the estimation of the pair potential function of Gibbs point processes

A flexible Bayesian method is suggested for pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoot hing technique, commonly applied in statistical image analysis. For the cal culation of the posterior mode estimator a new Monte Carlo algorithm is dev eloped. The method is illustrated through examples with both real and simul ated data, and its extension into truly nonparametric pair potential estima tion is discussed.