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

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A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variantstion for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible.

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Inference in Bayesian Networks Using Nested Junction Treesuch reductions. The usefulness of the method is emphasized through a thorough empirical evaluation involving ten large real-world Bayesian networks and both the Hugin and the Shafer-Shenoy inference algorithms.

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0258-123X rom a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject unde

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lanugo

Advanced Inference in Bayesian NetworksThe previous chapter introduced inference in discrete variable Bayesian networks. This used evidence propagation on the junction tree to find marginal distributions of interest. This chapter presents a tutorial introduction to some of the various types of calculations which can also be performed with the junction tree, specifically:

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NATO Science Series D:http://image.papertrans.cn/l/image/582963.jpg

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978-94-010-6104-9Springer Science+Business Media Dordrecht 1998

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https://doi.org/10.1007/978-94-011-5014-9Bayesian network; Latent variable model; Monte Carlo method; algorithms; clustering; data analysis; electr