[1]WANG Shuang-cheng,LI Xiao-lin,HOU Cai-hong.Learning in a hybrid Bayesian network structure for causal analysis[J].CAAI Transactions on Intelligent Systems,2007,2(6):82-90.
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Learning in a hybrid Bayesian network structure for causal analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 6 Page number: 82-90 Column: 学术论文—机器学习 Public date: 2007-12-25
Title:
Learning in a hybrid Bayesian network structure for causal analysis
Author(s):
WANG Shuang-cheng1;  LI Xiao-lin2;  HOU Cai-hong1
1. Department of Information Science, Shanghai Lixin University of Commerce, Sh anghai 201620, China;
?2. National Laboratory for Novel Software Technology, Nanj ing University, Nanjing 210093, China
Keywords:
causal analysis;  hybrid Bayesian network;  maximum likelihood tree;  Gibbs samplin g
CLC:
TP18
DOI:
-
Abstract:
At present, learning in a hybrid Bayesian network structure mainly depends on a combination of the searching & scoring method and the expanded entropy discretiz ation algorithm. However, the algorithm is prone to fall into local optimal trap s and its efficiency and reliability are not good. In this paper, a new iterativ e method for learning with hybrid Bayesian network structures is presented. In e ach iteration, mixed data clustering is carried out based on father mode structu res and Gibbs sampling, so that continuous variables are discretized. Then, thro ugh optimization of the Bayesian network structure, the sequence of Bayesian net work structures gradually tends to converge, avoiding the main problems encounte red with the expanded entropy discretization algorithm.
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