Loop corrections for message passing algorithms in continuous variable models

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In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, a different way of obtaining the covariances is found, based on Belief Propagation on cavity graphs. We discuss the relation of this loop correction algorithm to Expectation Propagation algorithms for the case in which the model is no longer Gaussian, but slightly perturbed by nonlinear terms.

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Artificial Intelligence, Machine Learning

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http://salesiana.dossiersoluciones.com/handle/123456789/129677

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0705.4566
 http://arxiv.org/abs/0705.4566

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arXiv