Graphical Models: Methods for Data Analysis and Mining
Brand: Wiley
Categories: Books, Science & Math, Mathematics



Dimensions: 6.1 x 1 x 9.1 inches
Language: English
ISBN-10: 0470843373
Publisher: Wiley
Print length: 368 pages
Publication date: March 15, 2002
ISBN-13: 978-0470843376
Edition: 1st
subtitle: 1st Edition
author: Visit Amazon's Christian Borgelt Page search results Christian Borgelt Rudolf Kruse
review: Editorial Reviews Review "...positioned at the boundary between two highly important research areas...not restricted to probabilistic models..." (Zentralblatt Math, 2003) "...a good and interesting book...every effort is made to make the concepts meaningful to the reader..." (Statistics in Medicine, Vol 23(11), 15 June 2004) From the Inside Flap The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data. One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example. * Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data. * Each concept is carefully explained and illustrated by examples. * Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions. * Features applications of learning graphical models from data, and problems for further research. * Includes a comprehensive bibliography. Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject. From the Back Cover The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a tool that allows the user to infer results from problems with imprecise data. One of the most important applications of graphical modelling today is data mining - the data-driven discovery and modelling of hidden patterns in large data sets. The techniques described have a wide range of industrial applications, and a quality testing programme at a major car manufacturer is included as a real-life example. * Provides a self-contained introduction to learning relational, probabilistic and possibilistic networks from data. * Each concept is carefully explained and illustrated by examples. * Contains all necessary background material, including modelling under uncertainty, decomposition of distributions, and graphical representation of decompositions. * Features applications of learning graphical models from data, and problems for further research. * Includes a comprehensive bibliography. Graphical Models: Methods for Data Analysis and Mining will be invaluable to researchers and practitioners who use graphical models in their work. Graduate students of applied statistics, computer science and engineering will find this book provides an excellent introduction to the subject. 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 is Professor for Computer Science at Otto-von-Guericke University of Magdeburg. Read more