Description - Graphical Models: Methods for Data Analysis and Mining by Christian Borgelt
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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