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Proceedings Paper

Stochastic gradient estimation strategies for Markov random fields
Author(s): Laurent Younes
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Paper 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.

Paper Details

Date Published: 22 September 1998
PDF: 11 pages
Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); doi: 10.1117/12.323811
Show Author Affiliations
Laurent Younes, Ecole Normale Superieure de Cachan (France)

Published in SPIE Proceedings Vol. 3459:
Bayesian Inference for Inverse Problems
Ali Mohammad-Djafari, Editor(s)

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