Stochastic gradient estimation strategies for Markov random fields

Laurent Younes

doi:10.1117/12.323811

22 September 1998 Stochastic gradient estimation strategies for Markov random fields

Laurent Younes

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323811
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Laurent Younes "Stochastic gradient estimation strategies for Markov random fields", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323811

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