  We present new message passing algorithms for performing inference with
graphical models. Our methods are designed for the most difficult inference
problems where loopy belief propagation and other heuristics fail to converge.
Belief propagation is guaranteed to converge when the underlying graphical
model is acyclic, but can fail to converge and is sensitive to initialization
when the underlying graph has complex topology. This paper describes
modifications to the standard belief propagation algorithms that lead to
methods that converge to unique solutions on graphical models with arbitrary
topology and potential functions.
