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The Scientific World Journal
Volume 2014, Article ID 749150, 12 pages
http://dx.doi.org/10.1155/2014/749150
Research Article

Chain Graph Models to Elicit the Structure of a Bayesian Network

Dipartimento di Statistica, Informatica, Applicazioni “G. Parenti”, Università degli Studi di Firenze, Viale Morgagni 59, 50134 Firenze, Italy

Received 31 August 2013; Accepted 5 November 2013; Published 5 February 2014

Academic Editors: R. M. Rodríguez-Dagnino and M. Saberi

Copyright © 2014 Federico M. Stefanini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.