A Hybrid Method for Orienting Edges of Bayesian Network
Dayou Liu · Dianzi xuebao · 2009
Bayesian network is one of the most important methods for representing and inferring with uncertainty knowledge, causal relation between variables is a key property for modeling the knowledge,so it is an important problem to orient the edges. There are some problems in the exist methods:(1)the computational complexity of the algorithms is high;(2)orienting the statistical indistinguishable edges may inconsistent with the domain knowledge.This paper presents an algorithm which combining the constraint approach and score search approach to orient the edges.The time of zero order and first order conditional independent test is polynomial;The search space can be decomposed to sub graph,which improving the efficiency of the algorithm.The output of the algorithm is largest chain graph,which just orienting the statistical distinguishable edges,keep the indistinguishable edges undirected. This graph present the causal relation more accuracy,and more convenient for combining domain knowledge.