Bayesian Belief Networks are graph-based representations of probability distributions. In the last decade they became popular for modeling and using uncertain knowledge in many and different contexts. In this paper an introduction to the framework and a review of the main issues related to learning Bayesian Belif Networks are presented. The first part focuses on the definition of the framework: the mathematical and representational properties are described and discussed as well as Belief Networks from data, a topic which received much attention recently. A large amount of works, approaches and methodologies proposed in the literature is surveyed

Bayesian Belief Networks: Introduction and Learning

Cattoni, Roldano;Potrich, Alessandra
1998

Abstract

Bayesian Belief Networks are graph-based representations of probability distributions. In the last decade they became popular for modeling and using uncertain knowledge in many and different contexts. In this paper an introduction to the framework and a review of the main issues related to learning Bayesian Belif Networks are presented. The first part focuses on the definition of the framework: the mathematical and representational properties are described and discussed as well as Belief Networks from data, a topic which received much attention recently. A large amount of works, approaches and methodologies proposed in the literature is surveyed
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/1513
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