Chain graph models to elicit the structure of a Bayesian network
type: Item
instance of: scholarly article
title: Chain graph models to elicit the structure of a Bayesian network
zbMATH Open document ID: 1328.62039
DOI: 10.1155/2014/749150
author: Federico M. Stefanini
published in: The Scientific World Journal. Probability and Statistics
publication date: 2016-01-13T00:00:00Z
review text: Summary: 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 way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by 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.
Mathematics Subject Classification ID: 62A09
Mathematics Subject Classification ID: 62C05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 68T35
zbMATH DE Number: 6529687
Wikidata QID: Q51098214
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/749150
OpenAlex ID: W2057909676
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description: scientific article; zbMATH DE number 6529687