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
1998-01-01
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 surveyedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
