Stochastic gradient estimation strategies for Markov random fields

This communication presents new results about convergence of stochastic gradient algorithms for maximum likelihood estimation of Markov random fields. We first present theoretical results dealing with the convergence of a generalized Robbins-Montro procedure. These results provide rigorous justifications for simple numerical strategies which can be employed in practice; they are illustrated by numerical experiments.