217,344 research outputs found
Mean Field Methods for a Special Class of Belief Networks
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
Consensus Propagation
We propose consensus propagation, an asynchronous distributed protocol for
averaging numbers across a network. We establish convergence, characterize the
convergence rate for regular graphs, and demonstrate that the protocol exhibits
better scaling properties than pairwise averaging, an alternative that has
received much recent attention. Consensus propagation can be viewed as a
special case of belief propagation, and our results contribute to the belief
propagation literature. In particular, beyond singly-connected graphs, there
are very few classes of relevant problems for which belief propagation is known
to converge.Comment: journal versio
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