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Graphical Models : Representations for Learning, Reasoning and Data Mining - Christian Borgelt

Graphical Models

Representations for Learning, Reasoning and Data Mining

By: Christian Borgelt, Matthias Steinbrecher, Rudolf R. Kruse

Hardcover | 7 August 2009 | Edition Number 2

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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 <i>Graphical Models</i> 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.
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?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.?  (Zentralblatt Math, 1 August 2013)

"All of the necessary background is provided, with material on modeling under uncertainty and imprecision modeling, decomposition of distributions, graphical representation of distributions, applications relating to graphical models, and problems for further research." (Book News, December 2009)

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