Subjective Bayesian methods for rule-based inference systems

Abstract

The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a reliance on the subjective judgment of human experts. We describe in this paper a subjective Bayesian inference method that realizes some of the advantages of both formal and informal approaches. Of particular interest are the modifications needed to deal with the inconsistencies usually found in collections of subjective statements.