Variant Bayesian networks

被引:0
作者
Peng, Qingsong [1 ]
Zhang, Ming
Wu, Weimin
Wang, Ronggui
机构
[1] Shanghai Maritime Univ, Dept Comp Sci & Technol, Shanghai 200135, Peoples R China
[2] SE Univ, Dept Comp Sci & Technol, Nanjing 210096, Peoples R China
来源
ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops | 2006年
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Bayesian networks can express the joint probabilistic distribution compactly between variables and can express the conditionally independence conveniently. The joint probabilistic influence from the parents to their child can be got from the Bayesian network structure however parents are not necessarily have common influence to their child, which are called by the name of causal influence independence other than conditional independence. The causal influence independence extension model of Bayesian networks presented can have wider meaning than traditional Bayesian networks, which is more applicable and easier to understand.
引用
收藏
页码:258 / 262
页数:5
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