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Approximation methods for efficient learning of Bayesian networks
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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| Lenguaje: | eng |
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IOS
2008
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| Acceso en línea: | http://cds.cern.ch/record/1991958 |