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Graphical Models: Methods for Data Analysis and Mining




Graphical Models: Methods for Data Analysis and Mining

Christian Borgelt, Rudolf Kruse

ISBN: 978-0-470-84337-6 February 2002 368 Pages


The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. 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 is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphical modelling is that specialised techniques that have been developed in one field can be transferred into others easily. The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer.
* 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, including modeling 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
An essential reference for graduate students of graphical modelling, applied statistics, computer science and engineering, as well as researchers and practitioners who use graphical models in their work.

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Imprecision and Uncertainty.


Graphical Representation.

Computing Projections.

Naive Classifiers.

Learning Global Structure.

Learning Local Structure.

Inductive Causation.


A. Proofs of Theorems.

B. Software Tools.


"...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)