The Unified Propagation and Scaling Algorithm

Yee Whye Teh, Max Welling

Abstract: In this paper we will show that a restricted class of constrained mini- mum 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 propaga- tion 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.