Bhattacharyya, C. ; Keerthi, S. S. (2001) Mean Field Methods for a Special Class of Belief Networks Journal of Artificial Intelligence Research, 15 . pp. 91-114.
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Official URL: https://doi.org/10.1613%2Fjair.734
Abstract
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 experiments show that the proposed schemes are attractive.
Item Type: | Article |
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ID Code: | 127749 |
Deposited On: | 13 Oct 2022 11:02 |
Last Modified: | 13 Oct 2022 11:02 |
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