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

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

Couverture rigide

ISBN 10 : 047072210X ISBN 13 : 9780470722107

Editeur : John Wiley & Sons Inc, 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

A propos du livre

Quatrième de couverture :

The use of graphical models in applied statistics has increasedconsiderably in recent years. At the same time the field of datamining has developed as a response to the large amounts ofavailable data. This book addresses the overlap between these twoimportant areas, highlighting the advantages of using graphicalmodels for data analysis and mining. The Authors focus not only onprobabilistic models such as Bayesian and Markov networks but alsoexplore relational and possibilistic graphical models in order toanalyse data sets.

• Presents all necessary background material includinguncertainty and imprecision modeling, distribution decompositionand graphical representation.
• Covers Markov, Bayesian, relational and possibilisticnetworks.
• Includes a new chapter on visualization and coverage of cliquetree propagation, visualization techniques.
• Demonstrates learning algorithms based on a large number ofdifferent search methods and evaluation measures.
• Includes a comprehensive bibliography and a detailedindex.
• Features an accompanying website hosting exercises, teachingmaterial and open source software.

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

Présentation de l'éditeur :

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.

À propos de cette édition

• Éditeur: John Wiley & Sons Inc
• Date d'édition: 2009
• ISBN 10: 047072210X
• ISBN 13: 9780470722107
• Reliure: Relié
• Numéro d'édition: 2
• Nombre de pages: 404