| 基于因果语义定向的贝叶斯网络结构学习 |
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| 引用本文： | 王双成,张明,陈乃激. 基于因果语义定向的贝叶斯网络结构学习[J]. 计算机工程与应用, 2007, 43(8): 29-31 |
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| 作者姓名： | 王双成 张明 陈乃激 |
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| 作者单位： | 上海立信会计学院,信息科学系,上海,201620;上海立信会计学院,中国立信风险管理研究院,上海,201620;上海立信会计学院,信息科学系,上海,201620 |
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| 基金项目： | 国家自然科学基金 , 上海市重点学科建设项目 , 上海市教委资助项目 |
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| 摘 要： | 基于变量之间基本依赖关系、基本结构、d-separation标准、依赖分析思想和混合定向策略,给出了一种有效实用的贝叶斯网络结构学习方法,不需要结点有序,并能避免打分-搜索方法存在的指数复杂性,以及现有依赖分析方法的大量高维条件概率计算等问题。
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| 关 键 词： | 贝叶斯网络 结构学习 依赖分析 因果语义 碰撞识别 |
| 文章编号： | 1002-8331（2007）08-0029-03 |
| 修稿时间： | 2006-08-01 |
Learning Bayesian networks structure based on causal semanitics orienting |
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| WANG Shuang-cheng,ZHANG Ming,CHEN Nai-ji. Learning Bayesian networks structure based on causal semanitics orienting[J]. Computer Engineering and Applications, 2007, 43(8): 29-31 |
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| Authors: | WANG Shuang-cheng ZHANG Ming CHEN Nai-ji |
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| Affiliation: | 1.Department of Information Science,Shanghai Lixin University of Commerce,Shanghai 201620,China ;2.Risk Management Research Institute,Shanghai Lixin University of Commerce,Shanghai 201620,China |
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| Abstract: | A new method of learning Bayesian network structure based on basic dependency relationship between variables,basic structure between nodes,d-separation criterion,the idea of dependency analysis and the strategy of mixture orienting is given.This method do not need sorting nodes.It can effectively avoid the exponential complexity of search & scoring based methods and a large number of the calculate of high rank conditional probability in existing dependency analysis based methods. |
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| Keywords: | Bayesian networks structure learning dependency analysis causal semanitics collider identification |
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