Structure Learning of Large Scale Bayesian Network

2014 ◽  
Author(s):  
Xiang Xu ◽  
Qing Liu ◽  
Yaping Li ◽  
Lin Xiao
Keyword(s):  
Bayesian Network ◽  
Large Scale ◽  
Structure Learning

scholarly journals Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ruo-Hai Di ◽  
Ye Li ◽  
Ting-Peng Li ◽  
Lian-Dong Wang ◽  
Peng Wang
Keyword(s):  
Dynamic Programming ◽  
Bayesian Network ◽  
Spanning Tree ◽  
Large Scale ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Maximum Weight ◽  
Significance Level ◽  
Bayesian Network Structure ◽  
Parents And Children

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.

scholarly journals Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 116661-116675 ◽  
Author(s):  
Yuguang Long ◽  
Limin Wang ◽  
Zhiyi Duan ◽  
Minghui Sun
Keyword(s):  
Bayesian Network ◽  
Structure Learning
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