Abstract：Bayesian network is a powerful knowledge representation and reasoning tool under uncertain conditions. Current algorithms for learning Bayesian networks structures are inefficient to a certain degree. Therefore,an efficient and reliable algorithm, ISOR, is proposed in this paper. Firstly, all the potential edges of the underlying network are produced by the maximum weight spanning tree algorithm and heuristic cut set searching algorithm. Then, methods based on identifying colliders and scoring search methods are integrated to orient all the edges in the network. Finally, redundant edges in the network are removed. Compared with other current algorithms based on dependency analysis, the proposed algorithm greatly reduces the number and the order of conditional independence tests. Algorithm analysis and experimental results on Alarm network show algorithm ISOR has good performance.
胡学钢，胡春玲. 一种基于依赖分析的贝叶斯网络结构学习算法*[J]. 模式识别与人工智能, 2006, 19(4): 445-449.
HU XueGang, HU ChunLing. A Dependency Analysis Based Algorithm for Learning Bayesian Networks. , 2006, 19(4): 445-449.
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2006, Vol. 19 
