An Algorithm for Cooperative Learning of Bayesian Network Structure from Data

2005 ◽  
pp. 86-94 ◽  
Author(s):  
Jiejun Huang ◽  
Heping Pan ◽  
Youchuan Wan
Keyword(s):  
Cooperative Learning ◽  
Bayesian Network ◽  
Network Structure ◽  
Bayesian Network Structure

scholarly journals An Algorithm Based on Loop Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 462
Author(s):  
Jie Wei ◽  
Yufeng Nie ◽  
Wenxian Xie
Keyword(s):  
Bayesian Inference ◽  
Bayesian Network ◽  
Network Structure ◽  
Computational Efficiency ◽  
Numerical Experiments ◽  
Global Perspective ◽  
Node Pair ◽  
Bayesian Network Structure ◽  
Network Structure Analysis ◽  
Contribution Index

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.

Bidirectional heuristic search to find the optimal Bayesian network structure

2021 ◽  
Vol 426 ◽  
pp. 35-46
Author(s):  
Xiangyuan Tan ◽  
Xiaoguang Gao ◽  
Zidong Wang ◽  
Chuchao He
Keyword(s):  
Bayesian Network ◽  
Network Structure ◽  
Heuristic Search ◽  
Bayesian Network Structure

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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