Titlebook: Learning in Graphical Models; Michael I. Jordan Book 1998 Springer Science+Business Media Dordrecht 1998 Bayesian network.Latent variable

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Latent Variable Modelsw of latent variable models for representing continuous variables. We show how a particular form of linear latent variable model can be used to provide a . formulation of the well-known technique of principal components analysis (PCA). By extending this technique to mixtures, and hierarchical mixtur

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Book 1998and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and des

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Michael I. Jordan,Zoubin Ghahramani,Tommi S. Jaakkola,Lawrence K. Saul

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