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

Macronutrients

Induction

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An Introduction to Variational Methods for Graphical Modelsxamples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, showing how upper and lower bounds can be f

高谈阔论

Improving the Mean Field Approximation Via the Use of Mixture Distributionshods make a completely factorized approximation to the posterior, which is unlikely to be accurate when the posterior is multimodal. Indeed, if the posterior is multi-modal, only one of the modes can be captured. To improve the mean field approximation in such cases, we employ mixture models as post

SLING

Introduction to Monte Carlo Methods high—dimensional problems such as arise in inference with graphical models. After the methods have been described, the terminology of Markov chain Monte Carlo methods is presented. The chapter concludes with a discussion of advanced methods, including methods for reducing random walk behaviour..For

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Glutinous

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才能

A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribu