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Mean-field methods for a special class of Belief Networks

Bhattacharyya, Chiranjib and Keerthi, Sathiya S (2001) Mean-field methods for a special class of Belief Networks. In: Journal Of Artificial Intelligence Research, 15 . pp. 91-114.

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The chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola, and Jordan's approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiements show that the proposed schemes are attractive.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to AI Access Foundation and Morgan Kaufmann.
Keywords: Distributions;Information;Algorithm;Model
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Mr. S Muthuraja
Date Deposited: 10 Feb 2010 06:45
Last Modified: 19 Sep 2010 04:55
URI: http://eprints.iisc.ac.in/id/eprint/17140

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