Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 146)
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Table of contents (17 chapters)
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Front Matter
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Propagation
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Learning
Keywords
About this book
In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
Editors and Affiliations
Bibliographic Information
Book Title: Advances in Bayesian Networks
Editors: José A. Gámez, Serafín Moral, Antonio Salmerón
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-540-39879-0
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2004
Hardcover ISBN: 978-3-540-20876-1Published: 23 February 2004
Softcover ISBN: 978-3-642-05885-1Published: 15 December 2010
eBook ISBN: 978-3-540-39879-0Published: 29 June 2013
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XI, 328
Topics: Probability Theory and Stochastic Processes, Mathematical and Computational Engineering, Artificial Intelligence, Pattern Recognition, Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences