Bayesian Networks: Learning

2015 ◽  
pp. 137-159
Author(s):  
Luis Enrique Sucar
Keyword(s):  
Bayesian Networks

Bayesian Networks Based Reliability Analysis of Phased-Mission Systems

2009 ◽  
Vol 31 (10) ◽  
pp. 1814-1825 ◽  
Author(s):  
Dong LIU ◽  
Chun-Yuan ZHANG ◽  
Wei-Yan XING ◽  
Rui LI
Keyword(s):  
Bayesian Networks ◽  
Reliability Analysis

Quantifying Systemic Risk Using Bayesian Networks

2020 ◽  
Author(s):  
Sumit Sourabh ◽  
Markus Hofer ◽  
Drona Kandhai
Keyword(s):  
Bayesian Networks ◽  
Systemic Risk

The impact of subway operation on urban traffic: A GRA-BNs based study

2021 ◽  
pp. 1-16
Author(s):  
Lixin Yan ◽  
Tao Zeng ◽  
Yubing Xiong ◽  
Zhenyun Li ◽  
Qingmei Liu
Keyword(s):  
Bayesian Networks ◽  
Operating Characteristic ◽  
Urban Traffic ◽  
Relational Analysis ◽  
Traffic Security ◽  
Structural Network ◽  
Random Decision Forest ◽  
Roc Area ◽  
Grey Relational ◽  
The Impact

With the development of urbanization, urban traffic has exposed many problems. To study the subway’s influence on urban traffic, this paper collects data on traffic indicators in Nanchang from 2008 to 2018. The research is carried out from three aspects: traffic accessibility, green traffic, and traffic security. First, Grey Relational Analysis is used to select 18 traffic indicators correlated with the subway from 22 traffic indicators. Second, the data is discretized and learned based on Bayesian Networks to construct the structural network of the subway’s influence. Third, to verify the reliability of using GRA and the effectiveness of Bayesian Networks (GRA-BNs), Bayesian Networks with full indicators analysis and other four algorithms (Naive Bayes, Random Decision Forest, Logistic and regression) are employed for comparison. Moreover, the receiver operating characteristic (ROC) area, true positive (TP) rate, false positive (FP) rate, precision, recall, F-measure, and accuracy are utilized for comparing each situation. The result shows that GRA-BNs is the most effective model to study the impact of the subway’s operation on urban traffic. Then, the dependence relations between the subway and each index are analyzed by the conditional probability tables (CPTs). Finally, according to the analysis, some suggestions are put forward.

Prediction of Risk Factors for Fall using Bayesian Networks with Partial Health Information

2020 ◽  
Author(s):  
Gulshan Sihag ◽  
Veronique Delcroix ◽  
Emmanuelle Grislin ◽  
Xavier Siebert ◽  
Sylvain Piechowiak ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bayesian Networks ◽  
Health Information
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