Bayesian Network Structure and Predictability of Autistic Traits

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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Bayesian network structure and predictability of autistic traits

10.31234/osf.io/83epj ◽

2020 ◽

Author(s):

Giovanni Briganti ◽

Donald Ray 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

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Learning Bayesian Network Structure from Small Data Set

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.01063 ◽

2009 ◽

Vol 35 (8) ◽

pp. 1063-1070 ◽

Cited By ~ 1

Author(s):

Shuang-Cheng WANG ◽

Cui-Ping LENG ◽

Xiao-Lin LI

Keyword(s):

Bayesian Network ◽

Network Structure ◽

Small Data ◽

Data Set ◽

Bayesian Network Structure ◽

Small Data Set

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Why BDeu ? Regular Bayesian network structure learning with discrete and continuous variables

Wiley Interdisciplinary Reviews Computational Statistics ◽

10.1002/wics.1554 ◽

2021 ◽

Author(s):

Joe Suzuki

Keyword(s):

Bayesian Network ◽

Network Structure ◽

Structure Learning ◽

Continuous Variables ◽

Bayesian Network Structure ◽

Bayesian Network Structure Learning

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

Mathematics ◽

10.3390/math9050462 ◽

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.

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