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Probabilistic Methods for Financial and Marketing Informatics
Richard E. Neapolitan, Xia Jiang
Verlag Elsevier Trade Monographs, 2007
ISBN 9780080555676 , 432 Seiten
Format PDF, ePUB, OL
Kopierschutz DRM
Geräte
Front Cover
1
Probabilistic Methods for Financial and Marketing Informatics
2
Copyright Page
3
Contents
8
Preface
4
Part I: Bayesian Networks and Decision Analysis
14
Chapter 1. Probabilistic Informatics
16
1.1 What Is Informatics?
17
1.2 Probabilistic Informatics
19
1.3 Outline of This Book
20
Chapter 2. Probability and Statistics
22
2.1 Probability Basics
22
2.2 Random Variables
29
2.3 The Meaning of Probability
37
2.4 Random Variables in Applications
43
2.5 Statistical Concepts
47
Chapter 3. Bayesian Networks
66
3.1 What Is a Bayesian Network?
67
3.2 Properties of Bayesian Networks
69
3.3 Causal Networks as Bayesian Networks
76
3.4 Inference in Bayesian Networks
85
3.5 How Do We Obtain the Probabilities?
91
3.6 Entailed Conditional Independencies *
105
Chapter 4. Learning Bayesian Networks
124
4.1 Parameter Learning
125
4.2 Learning Structure (Model Selection)
139
4.3 Score-Based Structure Learning *
140
4.4 Constraint-Based Structure Learning
151
4.5 Causal Learning
158
4.6 Software Packages for Learning
164
4.7 Examples of Learning
166
Chapter 5. Decision Analysis Fundamentals
190
5.1 Decision Trees
191
5.2 Influence Diagrams
208
5.3 Dynamic Networks *
225
Chapter 6. Further Techniques in Decision Analysis
242
6.1 Modeling Risk Preferences
243
6.2 Analyzing Risk Directly
249
6.3 Dominance
253
6.4 Sensitivity Analysis
257
6.5 Value of Information
267
6.6 Normative Decision Analysis
272
Part II: Financial Applications
278
Chapter 7. Investment Science
280
7.1 Basics of Investment Science
280
7.2 Advanced Topics in Investment Science*
291
7.3 A Bayesian Network Portfolio Risk Analyzer *
327
Chapter 8. Modeling Real Options
342
8.1 Solving Real Options Decision Problems
343
8.2 Making a Plan
352
8.3 Sensitivity Analysis
353
Chapter 9. Venture Capital Decision Making
356
9.1 A Simple VC Decision Model
358
9.2 A Detailed VC Decision Model
360
9.3 Modeling Real Decisions
363
9.A Appendix
365
Chapter 10. Bankruptcy Prediction
370
10.1 A Bayesian Network for Predicting Bankruptcy
371
10.2 Experiments
377
Part III: Marketing Applications
384
Chapter 11. Collaborative Filtering
386
11.1 Memory-Based Methods
387
11.2 Model-Based Methods
390
11.3 Experiments
393
Chapter 12. Targeted Advertising
400
12.1 Class Probability Trees
401
12.2 Application to Targeted Advertising
403
Bibliography
410
Index
422
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