Learning Dynamic Bayesian Network Structure from Non-time Symmetric Data

被引：0

作者：

Wang Shuang-cheng ^{[1
,3
]}

Shao Jun ^{[2
]}

Cheng Xin-zhang ^{[3
]}

机构：

[1] Shanghai Lixin Univ Commerce, Sch Math & Informat, Shanghai 201620, Peoples R China

[2] Shanghai Lixin Univ Commerce, Sch Accounting & Finance, Shanghai 201620, Peoples R China

[3] Shanghai Lixin Univ Commerce, Open Econ & Tracle Res Ctr, Shanghai 201620, Peoples R China

来源：

PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年

关键词：

dynamic Bayesian network; non-time symmetric data; transfer variable; structure learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

At present, there are not the methods of learning dynamic Bayesian network structure from no time symmetry data. In this paper, a method of learning dynamic Bayesian network structure from non-time symmetric data is developed by dint of transfer variables. In this method, first transfer variables between two adjacent time slices are learned by combining star structure and Gibbs sampling. Then dynamic Bayesian network part structure can be built based on sorting nodes and local search & scoring method. A complete dynamic Bayesian network structure can be obtained by extending along time series.

引用

页码：73 / +

页数：2

共 8 条

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