Global Conditioning for Probabilistic Inference in Belief Networks

In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple gener­ alization of Pearl's (1986b) method of loop­ cutset conditioning. We show that global conditioning, as well as loop--cutset condition­ ing, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (1990a; 199Gb). Nonetheless, this approach provides new op­ portunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) com­ bining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully.