- Hardcover: 368 pages
- Publisher: Wiley-Blackwell (6 Feb 2002)
- Language English
- ISBN-10: 0470843373
- ISBN-13: 978-0470843376
- Product Dimensions: 16 x 2.7 x 23.3 cm
- Amazon Bestsellers Rank: 3,470,914 in Books (See Top 100 in Books)
- See Complete Table of Contents
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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)
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)
Inside This Book
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First Sentence
Since this book is about graphical models and reasoning with them, we start by saying a few words about reasoning in general, with a focus on inferences under imprecision and uncertainty and the calculi to model these (cf. [Borgelt et al. 1998a]). Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
Since this book is about graphical models and reasoning with them, we start by saying a few words about reasoning in general, with a focus on inferences under imprecision and uncertainty and the calculi to model these (cf. [Borgelt et al. 1998a]). Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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1 review
11 of 13 people found the following review helpful
Good introduction, however focus on possibility
28 April 2004
Format:Hardcover
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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