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

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書目名稱Learning in Graphical Models
編輯Michael I. Jordan
視頻videohttp://file.papertrans.cn/583/582963/582963.mp4
叢書名稱NATO Science Series D:
圖書封面Titlebook: Learning in Graphical Models;  Michael I. Jordan Book 1998 Springer Science+Business Media Dordrecht 1998 Bayesian network.Latent variable
描述In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from 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 underlies the currentvolume. .Two research communities which have used graphical or networkformalisms to particular advantage are the .belief network.community and the .neural network. community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and 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
出版日期Book 1998
關(guān)鍵詞Bayesian network; Latent variable model; Monte Carlo method; algorithms; clustering; data analysis; electr
版次1
doihttps://doi.org/10.1007/978-94-011-5014-9
isbn_softcover978-94-010-6104-9
isbn_ebook978-94-011-5014-9Series ISSN 0258-123X
issn_series 0258-123X
copyrightSpringer Science+Business Media Dordrecht 1998
The information of publication is updating

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Introduction to Inference for Bayesian Networkslopments in many areas. As a consequence there is now a fairly large set of theoretical concepts and results for newcomers to the field to learn. This tutorial aims to give an overview of some of these topics, which hopefully will provide such newcomers a conceptual framework for following the more
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