Abstract
Abstract The Bayesian Network is a probabilistic graphical model in which a problem is structured as a set of parameters and probabilistic relationships among them. That is, a Bayesian Network for the domain represents a joint probability distribution over those variables. It has been used to effectively incorporate the expert knowledge and the historical data for updating the prior belief in the light of new evidence in many fields. However, few researchers have studied the comparison of different methods for resolving the inconsistency between priors and inference rules of a Bayesian Network. This study aimed to compare the existing methods that modified the inconsistency between priors and inference rules and use empirical data for demonstration. In particular, Chien et al. constructed a Bayesian Network on the basis of expert knowledge and historical data for fault diagnosis on distribution feeder in Taiwan. We also developed a new method to modify the inconsistency between priors and inference rules of a Bayesian Network for comparison. Keywords: Bayesian network, Inconsistency modification, Decision analysis, Knowledge management