Inference algorithms in Bayesian networks and the Probanet system

This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet-a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps filled in with regards to computability and completeness. Probanet has been designed and developed as a generic shell, a development environment for CPN construction and application. The design aspects and current status of Probanet are described. (C) 1998 Academic Press.