Graphical Models: Methods for Data Analysis and Mining Review

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(More customer reviews)The book gives a good, very deep introduction to the topic of Graphical models and data mining. The main focus is on the data mining section, thus the reader should have a basic knowledge about the graphical model concept. It is certainly not a beginner's book or a tutorial on graphical models or Bayesian networks. Furthermore the book is very mathematical with quite a lot of definitions, lemmas and proofs. A good knowledge in set theory is mandatory. However, the theory is very well explained and illustrated with simple examples.
At some points I would have been more interested in more practical issues, however this may be an engineers view. From my point of view, the main drawback of the book is the strong focus on possibility theory.
However, I highly recommend this book for everybody interested in Graphical Models and especially in reasoning with possibility theory instead of probability theory. The reader should bring a good mathematical background. Then the book does not only provide good examples, but a knowledge based on a strong mathematical formalism. This allows the reader to fully understand the topic. Reading this book takes time and a lot of effort, but you can certainly benefit more from it than from most other books about this topic.

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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 bibliographyAn 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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