%0 Journal Article
%T An Optimization Approach for Structural Learning Bayesian Networks Based on Prior Node Ordering<br>基于先验节点序学习贝叶斯网络结构的优化方法
%A ZHU Ming-Min
%A LIU San-Yang
%A WANG Chun-Feng
%A <br>朱明敏
%A 刘三阳
%A 汪春峰
%J 自动化学报
%D 2011
%I 
%X To solve the drawbacks of learning Bayesian networks (BN) from small data set and the unreliability of the conditional independence (CI) tests when the conditioning sets become too large, this paper proposes an optimization approach for structural learning Bayesian networks based on prior node ordering. It is the first time that a problem of structural learning for a Bayesian network is transformed into its related mathematical programming problem by defining objective function and feasible region. And, we have proved the existence and uniqueness of the numerical solution. The approach offers a new opinion for the research of extended Bayesian networks. Theoretical and experimental results show that the new approach is correct and effective.
%K Bayesian network (BN)
%K optimization model
%K conditional independence test
%K structure learning
%K node ordering<br>贝叶斯网络
%K 优化模型
%K 条件独立测试
%K 结构学习
%K 节点序
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=8207906E12F691D6008369722EFA8E1B&yid=9377ED8094509821&vid=42425781F0B1C26E&iid=59906B3B2830C2C5&sid=B7C6D333F9B9ED14&eid=DEEC1AC3B6D3EB96&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=20