Approximation Methods for Efficient Learning of Bayesian Networks.

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This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Riggelsen, C.
Formato: eBook
Idioma:Inglés
Publicado: Amsterdam : IOS Press, 2008.
Colección:Frontiers in artificial intelligence and applications.
Temas:
Bayesian statistical decision theory.
Machine learning.
Neural networks (Computer science)
Neural Networks, Computer
Machine Learning
Théorie de la décision bayésienne.
Apprentissage automatique.
Réseaux neuronaux (Informatique)
Bayesian statistical decision theory
Machine learning
Acceso en línea:Texto completo
Descripción
Sumario:This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order t.
Descripción Física:1 online resource (148 pages).
ISBN:1586038214
9781586038212

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