Negative Tree Reweighted Belief Propagation

Details Negative Tree Reweighted Belief Propagation Negative Tree Reweighted Belief Propagation

2012-03-15

arxiv.org/abs/1203.3494v1

Computer Science

Learning

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

Scientific paper

We introduce a new class of lower bounds on the log partition function of a Markov random field which makes use of a reversed Jensen's inequality. In particular, our method approximates the intractable distribution using a linear combination of spanning trees with negative weights. This technique is a lower-bound counterpart to the tree-reweighted belief propagation algorithm, which uses a convex combination of spanning trees with positive weights to provide corresponding upper bounds. We develop algorithms to optimize and tighten the lower bounds over the non-convex set of valid parameter values. Our algorithm generalizes mean field approaches (including naive and structured mean field approximations), which it includes as a limiting case.

Affiliated with

Also associated with

No associations

Rating

Negative Tree Reweighted Belief Propagation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Negative Tree Reweighted Belief Propagation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Negative Tree Reweighted Belief Propagation will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-32232