An iterative inference mechanism for the probabilistic expert system PES

The paper suggests a new inference mechanism based on iterative use of the Bayesian inference scheme. The procedure iteratively computes optimal compo nent weights of a distribution mixture from a class called generalized know ledge base. It is proved that the iterative process converges to a unique l imit whereby the resulting probability distribution can be defined as the i nformation-divergence projection of input distribution on the generalized k nowledge base. The iterative inference mechanism resembles natural process of cognition as iteratively improving understanding of the input informatio n.