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Graphical Models: Representations for Learning, Reasoning and Data Mining / Edition 2

Graphical Models: Representations for Learning, Reasoning and Data Mining / Edition 2


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Product Details

ISBN-13: 9780470722107
Publisher: Wiley
Publication date: 10/12/2009
Series: Wiley Series in Computational Statistics Series , #704
Edition description: Second Edition
Pages: 404
Product dimensions: 6.20(w) x 9.10(h) x 1.00(d)

About the Author

Christian Borgelt, is the Principal researcher at the European Centre for Soft Computing at Otto-von-Guericke University of Magdeburg.

Rudolf Kruse, Professor for Computer Science at Otto-von-Guericke University of Magdeburg.

Matthias Steinbrecher, Department of Knowledge Processing and Language Engineering, School of Computer Science, Universitätsplatz 2, Magdeburg, Germany.

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Table of Contents


1 Introduction.

1.1 Data and Knowledge.

1.2 Knowledge Discovery and Data Mining.

1.3 Graphical Models.

1.4 Outline of this Book.

2 Imprecision and Uncertainty.

2.1 Modeling Inferences.

2.2 Imprecision and Relational Algebra.

2.3 Uncertainty and Probability Theory.

2.4 Possibility Theory and the Context Model.

3 Decomposition.

3.1 Decomposition and Reasoning.

3.2 Relational Decomposition.

3.3 Probabilistic Decomposition.

3.4 Possibilistic Decomposition.

3.5 Possibility versus Probability.

4 Graphical Representation.

4.1 Conditional Independence Graphs.

4.2 Evidence Propagation in Graphs.

5 Computing Projections.

5.1 Databases of Sample Cases.

5.2 Relational and Sum Projections.

5.3 Expectation Maximization.

5.4 Maximum Projections.

6 Naive Classifiers.

6.1 Naive Bayes Classifiers.

6.2 A Naive Possibilistic Classifier.

6.3 Classifier Simplification.

6.4 Experimental Evaluation.

7 Learning Global Structure.

7.1 Principles of Learning Global Structure.

7.2 Evaluation Measures.

7.3 Search Methods.

7.4 Experimental Evaluation.

8 Learning Local Structure.

8.1 Local Network Structure.

8.2 Learning Local Structure.

8.3 Experimental Evaluation.

9 Inductive Causation.

9.1 Correlation and Causation.

9.2 Causal and Probabilistic Structure.

9.3 Faithfulness and Latent Variables.

9.4 The Inductive Causation Algorithm.

9.5 Critique of the Underlying Assumptions.

9.6 Evaluation.

10 Visualization.

10.1 Potentials.

10.2 Association Rules.

11 Applications.

11.1 Diagnosis of Electrical Circuits.

11.2 Application in Telecommunications.

11.3 Application at Volkswagen.

11.4 Application at DaimlerChrysler.

A Proofs of Theorems.

A.1 Proof of Theorem 4.1.2.

A.2 Proof of Theorem 4.1.18.

A.3 Proof of Theorem 4.1.20.

A.4 Proof of Theorem 4.1.26.

A.5 Proof of Theorem 4.1.28.

A.6 Proof of Theorem 4.1.30.

A.7 Proof of Theorem 4.1.31.

A.8 Proof of Theorem 5.4.8.

A.9 Proof of Lemma .2.2.

A.10 Proof of Lemma .2.4.

A.11 Proof of Lemma .2.6.

A.12 Proof of Theorem 7.3.1.

A.13 Proof of Theorem 7.3.2.

A.14 Proof of Theorem 7.3.3.

A.15 Proof of Theorem 7.3.5.

A.16 Proof of Theorem 7.3.7.

B Software Tools.



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"All of the necessary background is provided, with material on modeling under uncertainty and imprecision modeling, decomposition of distributions, graphical representation of distributions, applications relating to graphical models, and problems for further research." (Book News, December 2009)

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