Research on Explanation Function for Reason Conclusions with Bayesian Network

In this paper, an explanation function about Bayesian network is presented. With it, evidences' effect degree, direction and paths on reason conclusion can be explained. Necessity factor and sufficiency factor are designed as a measure approach, to valuate evidences' effect degree on posteriori distributions. By the way of qualitatively analysis the character of network structure, notes relative to reason conclusion are find out. Based on those notes, and combined with the quantitatively analysis, sub chains which consist of effect paths are found out, too. Those sub chains are valuated to create and explain the effect paths. Experiment results show the effectiveness of the explain function.