Minimum Information Updating With Specified Marginals in Probabilistic Expert Systems

Abstract

A probability-updating method in probabilistic expert systems is considered in this paper based on minimum discrimination information. Here, newly acquired information is taken as the latest true marginal probabilities, not as observed data with the same weight as previous data. Posterior probabilities are obtained by updating prior probabilities subject to the latest true marginals. To apply this updating method to probabilistic expert systems, we extend Ku and Kullback(1968)'s minimum discrimination information method for saturated models to log-linear models, discuss localization of global updating, and show that Deming and Stephan's iterative procedure can be used to find the posterior probabilities. Our updating method can also be used to handle uncertain evidence in probabilistic expert systems.