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

Borgelt, Christian; Steinbrecher, Matthias; Kruse, Rudolf

Rilegato

ISBN 10: 047072210X ISBN 13: 9780470722107

Casa editrice: John Wiley & Sons Inc, 2009

Riassunto

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.

Su questo libro

Recensione

The text provides graduate students, and researchers withall the necessary background material, including modelling underuncertainty, decomposition of distributions, graphicalrepresentation of distributions, and applications relating tographical models and problems for further research.  (Zentralblatt Math, 1 August 2013)

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

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: John Wiley & Sons Inc
• Data di pubblicazione: 2009
• ISBN 10: 047072210X
• ISBN 13: 9780470722107
• Rilegatura: Copertina rigida
• Lingua: Inglese
• Numero edizione: 2
• Numero di pagine: 393