Computational models for probabilistic reasoning in expert systems

Numerous methods have been suggested and used for dealing with uncertainty in expert systems. Work in theoretical statistics has shown that it is possible to adopt sound probabilistic approaches to uncertain inference in expert systems. The author considers two approaches: the first uses Bayesian inference without assuming independence and the second is based on causal graphs, or more correctly termed `influence diagrams´