Graphical Models: Representations for Learning, Reasoning and Data Mining (Wiley Series in Computational Statistics): 704 - Hardcover

Borgelt, Christian; Steinbrecher, Matthias; Kruse, Rudolf R.

Hardcover

ISBN 10: 047072210X ISBN 13: 9780470722107

Publisher: Wiley, 2009

Synopsis

Graphical Models 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. Full description

About this title

Review:

"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)

From the Back Cover:

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 important areas, highlighting the advantages of using graphical models for data analysis and mining. The Authors focus not only on probabilistic models such as Bayesian and Markov networks but also explore relational and possibilistic graphical models in order to analyse data sets.

• Presents all necessary background material including uncertainty and imprecision modeling, distribution decomposition and graphical representation.
• Covers Markov, Bayesian, relational and possibilistic networks.
• Includes a new chapter on visualization and coverage of clique tree propagation, visualization techniques.
• Demonstrates learning algorithms based on a large number of different search methods and evaluation measures.
• Includes a comprehensive bibliography and a detailed index.
• Features an accompanying website hosting exercises, teaching material and open source software.

Researchers and practitioners who use graphical models in their work, graduate students of applied statistics, computer science and engineering will find much of interest in this new edition.

About this edition

• Publisher: Wiley
• Publication date: 2009
• ISBN 10: 047072210X
• ISBN 13: 9780470722107
• Binding: Hardcover
• Edition number: 2
• Number of pages: 404