Conversion of belief networks into belief rules: A new approach

Abstract

This paper shows a new method of explaining Bayesian networks by creating descriptions of their properties in a manner closer to the human perceptual abilities, i.e., decision rules in the IF...THEN form (called by us belief rules). The conversion method is based on the cause and effect analysis of the Bayesian network quantitative component (the probability distribution). Proposed analysis of the quantitative component leads to a deeper insight into the structure of knowledge hidden in the analyzed data set.