The structure of a Bayesian Network is a priori plausible if the directed acyclic graph has one or more plausible structural features. Expert beliefs about the structure of a Bayesian Network may be substantial but limited both to a subset of nodes or to a set of network features indirectly related to network edges. Complex elicitation tasks involving dozens of reference features may be cognitively too diffi- cult for the expert, unless limited subsets of features may be considered at one time. In this paper chain graph models on descriptors of structural features are proposed as a tool to elicit the degree of belief associated to the structure of a Bayesian Network. An algorithm and a parameterization are developed to support the elicitation.

Graphical models for eliciting structural information / F.M.Stefanini. - STAMPA. - (2010), pp. 1-2. (Intervento presentato al convegno Classification and Data Analysis Group of the Italian Statistical Society tenutosi a Firenze nel Settembre 2010).

Graphical models for eliciting structural information

STEFANINI, FEDERICO MATTIA
2010

Abstract

The structure of a Bayesian Network is a priori plausible if the directed acyclic graph has one or more plausible structural features. Expert beliefs about the structure of a Bayesian Network may be substantial but limited both to a subset of nodes or to a set of network features indirectly related to network edges. Complex elicitation tasks involving dozens of reference features may be cognitively too diffi- cult for the expert, unless limited subsets of features may be considered at one time. In this paper chain graph models on descriptors of structural features are proposed as a tool to elicit the degree of belief associated to the structure of a Bayesian Network. An algorithm and a parameterization are developed to support the elicitation.
2010
GfKI-Cladag 2010. Book of Abstracts
Classification and Data Analysis Group of the Italian Statistical Society
Firenze
F.M.Stefanini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/394735
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