Inference by belief propagation in composite systems.
We devise a message passing algorithm for probabilistic inference in composite systems, consisting of a large number of variables, that exhibit weak random interactions among all variables and strong interactions with a small subset of randomly chosen variables; the relative strength of the two interactions is controlled by a free parameter. We examine the performance of the algorithm numerically on a number of systems of this type for varying mixing parameter values.
Neural Computing Research Group, Aston University, Birmingham B4 7ET, United Kingdom.
Etienne
E
Mallard
David
D
Saad