The exploitation of prior information is often advocated to face the curse of dimensionality which affects multivariate statistical models in biomedical research and bioinformatics. In a recent contribution dealing with score-and-search learning of Bayesian Networks, the elicitation of beliefs in large spaces of structures has been con- sidered in a general setup based on elicited network features. In this contribution we build on the above cited work and provide a simpler formalization dealing with incomplete prior information in the form of DAG of network features. A case study illustrates the proposed approach.

Prior beliefs about the structure of a probabilistic network / F. M. Stefanini. - STAMPA. - (2009), pp. 1-4. ((Intervento presentato al convegno SIS 2009 -Statistical methods fo the analysis of large data-sets tenutosi a Pescara nel Settembre 2009.

Prior beliefs about the structure of a probabilistic network

STEFANINI, FEDERICO MATTIA
2009

Abstract

The exploitation of prior information is often advocated to face the curse of dimensionality which affects multivariate statistical models in biomedical research and bioinformatics. In a recent contribution dealing with score-and-search learning of Bayesian Networks, the elicitation of beliefs in large spaces of structures has been con- sidered in a general setup based on elicited network features. In this contribution we build on the above cited work and provide a simpler formalization dealing with incomplete prior information in the form of DAG of network features. A case study illustrates the proposed approach.
SIS 2009 -Statistical methods fo the analysis of large data-sets - Proceedings
SIS 2009 -Statistical methods fo the analysis of large data-sets
Pescara
Settembre 2009
F. M. Stefanini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/370737
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