Graphical Models: Representations for Learning, Reasoning and Data Mining
Borgelt, Christian/ Steinbrecher, Matthias/ Kruse, Rudolf
ISBN 10: 047072210X ISBN 13: 9780470722107
Published by John Wiley & Sons Inc, 2009
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2nd edition. 408 pages. 9.21x6.22x1.02 inches. In Stock. Seller Inventory # __047072210X
Synopsis:
Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.
About the Author:
Christian Borgelt, is the Principal researcher at the European Centre for Soft Computing at Otto-von-Guericke University of Magdeburg.
Rudolf Kruse, Professor for Computer Science at Otto-von-Guericke University of Magdeburg.
Matthias Steinbrecher, Department of Knowledge Processing and Language Engineering, School of Computer Science, Universitätsplatz 2,?Magdeburg, Germany.
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Title: Graphical Models: Representations for ...
Publisher: John Wiley & Sons Inc
Publication Date: 2009
Binding: Hardcover
Condition: Brand New
Edition: 2nd Edition
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Condition: New. The use of graphical models in applied statistics has increased considerably in recent years. At the same time the field of data mining has developed as a response to the large amounts of available data. This book addresses the overlap between these two imp. Seller Inventory # 556557868
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ISBN 10: 047072210X
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Hardcover. Condition: new. Hardcover. Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research. Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data All basic concepts carefully explained and illustrated by examples throughout Contains background material including graphical representation, including Markov and Bayesian Networks. Includes a comprehensive bibliography. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780470722107
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Buch. Condition: Neu. Neuware - Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research. Seller Inventory # 9780470722107
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