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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.