DocumentCode :
3259758
Title :
Variant Bayesian Networks
Author :
Qingsong, Peng ; Ming, Zhang ; Weimin, Wu ; Ronggui, Wang
Author_Institution :
Dept. of Comput. Sci. & Technol., Shanghai Maritime Univ.
fYear :
2006
fDate :
Dec. 2006
Firstpage :
258
Lastpage :
262
Abstract :
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
Keywords :
belief networks; statistical distributions; Bayesian networks; causal influence independence; joint probabilistic distribution; Artificial intelligence; Bayesian methods; Computer science; Decision making; Graphical models; Machine learning; Markov random fields; Probability distribution; Random variables; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
Type :
conf
DOI :
10.1109/ICDMW.2006.169
Filename :
4063635
Link To Document :
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