Christian Borgelt, Matthias Steinbrecher, Rudolf Kruse
Graphical Models
Representations for Learning, Reasoning and Data Mining
Christian Borgelt, Matthias Steinbrecher, Rudolf Kruse
Graphical Models
Representations for Learning, Reasoning and Data Mining
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Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of…mehr
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Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distributions, and applications relating to graphical models and problems for further research.
Produktdetails
- Produktdetails
- Wiley Series in Computational Statistics
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14572210000
- 2. Aufl.
- Seitenzahl: 404
- Erscheinungstermin: 15. September 2009
- Englisch
- Abmessung: 236mm x 160mm x 28mm
- Gewicht: 724g
- ISBN-13: 9780470722107
- ISBN-10: 047072210X
- Artikelnr.: 26109490
- Wiley Series in Computational Statistics
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14572210000
- 2. Aufl.
- Seitenzahl: 404
- Erscheinungstermin: 15. September 2009
- Englisch
- Abmessung: 236mm x 160mm x 28mm
- Gewicht: 724g
- ISBN-13: 9780470722107
- ISBN-10: 047072210X
- Artikelnr.: 26109490
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.
Preface. 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.
Bibliography. Index.
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.
Bibliography. Index.
Preface. 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.
Bibliography. Index.
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.
Bibliography. Index.