The Unified Propagation and Scaling Algorithm

In this paper we will show that a restricted class of constrained minimum divergence problems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propagation and iterative scaling. This unified propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Yee Whye Teh, Max Welling
• Keywords: Constrained Minimum Divergence, KL Divergence, Loopy Belief Propagation, NIPS 2001, NIPS 2007