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, Universitatsplatz 2,?Magdeburg, Germany.

Reviews

"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)




Book Information
ISBN 9780470722107
Author Christian Borgelt
Format Hardback
Page Count 404
Imprint John Wiley & Sons Inc
Publisher John Wiley & Sons Inc
Weight(grams) 712g
Dimensions(mm) 236mm * 159mm * 27mm