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.

Chain Graph Models to Elicit the Structure of a Bayesian Network / F.M. Stefanini. - In: THE SCIENTIFIC WORLD JOURNAL. - ISSN 2356-6140. - 2014(2014 Feb 05), pp. 749150.1-749150.12.

Chain Graph Models to Elicit the Structure of a Bayesian Network

F.M. Stefanini
2014

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.
Structural inference; Probabilistic networks; Bayesian elicitation
Settore SECS-S/01 - Statistica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/849324
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