  The paper presents an iterative version of join-tree clustering that applies
the message passing of join-tree clustering algorithm to join-graphs rather
than to join-trees, iteratively. It is inspired by the success of Pearl's
belief propagation algorithm as an iterative approximation scheme on one hand,
and by a recently introduced mini-clustering i. success as an anytime
approximation method, on the other. The proposed Iterative Join-graph
Propagation IJGP belongs to the class of generalized belief propagation
methods, recently proposed using analogy with algorithms in statistical
physics. Empirical evaluation of this approach on a number of problem classes
demonstrates that even the most time-efficient variant is almost always
superior to IBP and MC i, and is sometimes more accurate by as much as several
orders of magnitude.
