A Method of Computing Generalized Bayesian Probability Values for Expert Systems

Abstract

This paper presents a new method for calculating the conditional probability of any multi-valued predicate given particular information about the individual case. This calculation is based on the principle of Maximum Entropy (ME), sometimes called the principle of least information, and gives the most unbiased probability estimate given the available evidence. Previous methods for computing maximum entropy values shows that they are either very restrictive in the probabilistic information (constraints) they can use or combinatorially explosive. The computational complexity of the new procedure depends on the inter-connectedness of the constraints, but in practical cases it is small. In addition, the maximum entropy method can give a measure of how accurately a calculated conditional probability is known.