Chain Graph Models to Elicit the Structure of a Bayesian Network
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Bayesian networks are possibly the most successful graphical models to build decision support systems.
Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative waywhile learning the network structure from data.
In this paper details are provided about how to build a prior distribution on the space of network structuresby eliciting a chain graph model on structural reference features.
Several structural features expected to be often useful during the elicitation are described.
The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated.
Finally, a few seminal contributions from the literature are reformulated in terms of structural features.
American Psychological Association (APA)
Stefanini, Federico M.. 2014. Chain Graph Models to Elicit the Structure of a Bayesian Network. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050896
Modern Language Association (MLA)
Stefanini, Federico M.. Chain Graph Models to Elicit the Structure of a Bayesian Network. The Scientific World Journal No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1050896
American Medical Association (AMA)
Stefanini, Federico M.. Chain Graph Models to Elicit the Structure of a Bayesian Network. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1050896
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050896