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具有传递变量的动态贝叶斯网络结构学习 被引量：3

Learning dynamic Bayesian network structure with transfer variables
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摘要 针对现有学习方法对完全时间不对称数据的动态贝叶斯网络学习不具有实用性,提出一种借助传递变量进行完全时间不对称数据的动态贝叶斯网络结构学习方法.首先进行相邻时间片间的传递变量序列学习;然后,基于节点排序和局部打分-搜索,进行动态贝叶斯网络局部结构学习;最后通过时序扩展得到整个动态贝叶斯网络结构. At present,there are not the methods of learning dynamic Bayesian network structure from no time symmetry data.Therefore,a method of learning dynamic Bayesian network structure from no time symmetry data is developed by combining transfer variables.In this method,first transfer variable series between two adjacent time slices are found.Then dynamic Bayesian network part structure can be learned based on sorting nodes and local search and scoring method.Finally, a whole dynamic Bayesian network structure can be presented by extending along time series.
作者 王双成 王辉 许广林
机构地区 上海立信会计学院数学与信息学院开放经济与贸易研究中心 中央民族大学信息工程学院 上海立信会计学院数学与信息学院
出处 《控制与决策》 EI CSCD 北大核心 2010年第11期1737-1741,1746,共6页 Control and Decision
基金 国家自然科学基金项目(60675036) 上海市教委重点学科建设项目(J51702) 上海市教委科研创新重点项目(09zz202)
关键词 动态贝叶斯网络 完全时间不对称数据 传递变量 结构学习 Dynamic Bayesian network No time symmetry data Transfer variable Structure learning
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献9

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共引文献83

同被引文献22

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控制与决策
2010年 第11期

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