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Titlebook: Innovations in Bayesian Networks; Theory and Applicati Dawn E. Holmes,Lakhmi C. Jain (Professor of Knowle Book 2008 Springer-Verlag Berlin

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發(fā)表于 2025-3-21 18:49:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Innovations in Bayesian Networks
副標題Theory and Applicati
編輯Dawn E. Holmes,Lakhmi C. Jain (Professor of Knowle
視頻videohttp://file.papertrans.cn/468/467035/467035.mp4
概述Presents the innovative paradigms related to the theory and practical applications of Bayesian Networks
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Innovations in Bayesian Networks; Theory and Applicati Dawn E. Holmes,Lakhmi C. Jain (Professor of Knowle Book 2008 Springer-Verlag Berlin
描述.Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained....Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research..
出版日期Book 2008
關(guān)鍵詞Bayesian Networks; Bayesian network; Learning Bayesian networks; Topologie; artificial intelligence; comp
版次1
doihttps://doi.org/10.1007/978-3-540-85066-3
isbn_softcover978-3-642-09875-8
isbn_ebook978-3-540-85066-3Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2008
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Dawn E. Holmes,Lakhmi C. Jain (Professor of KnowlePresents the innovative paradigms related to the theory and practical applications of Bayesian Networks
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Studies in Computational Intelligencehttp://image.papertrans.cn/i/image/467035.jpg
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The Causal Interpretation of Bayesian Networks, human understanding and with computational mechanisms supportive of probabilistic reasoning (updating). But the interpretation of Bayesian networks assumed by causal discovery algorithms is causal: the links in the graphs specifically represent direct causal connections between variables. However,
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