A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images

Various theoretical and computational issues about the algorithms´ behavior are addressed. The concept of relative entropy is introduced as the primary analytical tool, and convergence of the simulation algorithms is discussed in terms of the relative entropy. This approach allows a view of the simulation of Gibbs random field images as a constrained, convex optimization problem, and it results in a systematic study of various Monte Carlo simulation algorithms under a common analytical framework. The problems of proper initialization, of maximizing the rate of convergence at each iteration, and of minimizing the rejection rate are discussed. A computational comparison of various Monte Carlo simulation algorithms is also presented