U-BASE: General Bayesian Network-Driven Context Prediction for Decision Support

2010 ◽  
pp. 63-72
Author(s):  
Kun Chang Lee ◽  
Heeryon Cho ◽  
Sunyoung Lee
Keyword(s):  
Decision Support ◽  
Bayesian Network

Bayesian network based decision support for predicting and mitigating delay risk in TBM tunnel projects

2021 ◽  
Vol 129 ◽  
pp. 103819
Author(s):  
G.C. Koseoglu Balta ◽  
I. Dikmen ◽  
M.T. Birgonul
Keyword(s):  
Decision Support ◽  
Bayesian Network

scholarly journals Bridging groundwater models and decision support with a Bayesian network

2013 ◽  
Vol 49 (10) ◽  
pp. 6459-6473 ◽  
Author(s):  
Michael N. Fienen ◽  
John P. Masterson ◽  
Nathaniel G. Plant ◽  
Benjamin T. Gutierrez ◽  
E. Robert Thieler
Keyword(s):  
Decision Support ◽  
Bayesian Network

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

An Ontology Driven and Bayesian Network Based Cardiovascular Decision Support Framework

2012 ◽  
pp. 31-41 ◽  
Author(s):  
Kamran Farooq ◽  
Amir Hussain ◽  
Stephen Leslie ◽  
Chris Eckl ◽  
Calum MacRae ◽  
...  
Keyword(s):  
Decision Support ◽  
Bayesian Network ◽  
Decision Support Framework

Widely Applicable Multi-Variate Decision Support Model for Market Trend Analysis and Prediction with Case Study in Retail

2014 ◽  
pp. 989-1018 ◽  
Author(s):  
Leo Mršić
Keyword(s):  
Time Series ◽  
Decision Support ◽  
Bayesian Network ◽  
Real Life ◽  
Heterogeneous Data Source ◽  
Decision Support Model ◽  
Market Trends ◽  
Series Transformation ◽  
Support Model

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.

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