Graphical Models: Methods for Data Analysis and Mining
* 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.
Imprecision and Uncertainty.
Learning Global Structure.
Learning Local Structure.
A. Proofs of Theorems.
B. Software Tools.
"...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)