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Probabilistic Methods for Financial and Marketing Informatics

Book Description

Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications, ans

* Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance.

* Shares insights about when and why probabilistic methods can and cannot be used effectively;

* Complete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. About the Authors
  7. I: Bayesian Networks and Decision Analysis
    1. Chapter 1: Probabilistic Informatics
      1. 1.1 What Is Informatics?
      2. 1.2 Probabilistic Informatics
      3. 1.3 Outline of This Book
    2. Chapter 2: Probability and Statistics
      1. 2.1 Probability Basics
      2. 2.2 Random Variables
      3. 2.3 The Meaning of Probability
      4. 2.4 Random Variables in Applications
      5. 2.5 Statistical Concepts
      6. EXERCISES
    3. Chapter 3: Bayesian Networks
      1. 3.1 What Is a Bayesian Network?
      2. 3.2 Properties of Bayesian Networks
      3. 3.3 Causal Networks as Bayesian Networks
      4. 3.4 Inference in Bayesian Networks
      5. 3.5 How Do We Obtain the Probabilities?
      6. 3.6 Entailed Conditional Independencies*
      7. EXERCISES
      8. Section 3.1
      9. Section 3.2
      10. Section 3.3
      11. Section 3.4
      12. Section 3.5
    4. Chapter 4: Learning Bayesian Networks
      1. 4.1 Parameter Learning
      2. 4.2 Learning Structure (Model Selection)
      3. 4.3 Score-Based Structure Learning*
      4. 4.4 Constraint-Based Structure Learning
      5. 4.5 Causal Learning
      6. 4.6 Software Packages for Learning
      7. 4.7 Examples of Learning
      8. EXERCISES
      9. Section 4.1
      10. Section 4.3
      11. Section 4.4
      12. Section 4.5
      13. Section 4.6
      14. Section 4.7
    5. Chapter 5: Decision Analysis Fundamentals
      1. 5.1 Decision Trees
      2. 5.2 Influence Diagrams
      3. 5.3 Dynamic Networks*
      4. EXERCISES
      5. Section 5.1
      6. Section 5.2
      7. Section 5.3
    6. Chapter 6: Further Techniques in Decision Analysis
      1. 6.1 Modeling Risk Preferences
      2. 6.2 Analyzing Risk Directly
      3. 6.3 Dominance
      4. 6.4 Sensitivity Analysis
      5. 6.5 Value of Information
      6. 6.6 Normative Decision Analysis
      7. EXERCISES
      8. Section 6.1
      9. Section 6.2
      10. Section 6.3
      11. Section 6.4
      12. Section 6.5
  8. II: Financial Applications
    1. Chapter 7: Investment Science
      1. 7.1 Basics of Investment Science
      2. 7.2 Advanced Topics in Investment Science*
      3. 7.3 A Bayesian Network Portfolio Risk Analyzer*
      4. EXERCISES
      5. Section 7.1
      6. Section 7.2
      7. Section 7.3
    2. Chapter 8: Modeling Real Options
      1. 8.1 Solving Real Options Decision Problems
      2. 8.2 Making a Plan
      3. 8.3 Sensitivity Analysis
      4. EXERCISES
      5. Section 8.1
      6. Section 8.2
      7. Section 8.3
    3. Chapter 9: Venture Capital Decision Making
      1. 9.1 A Simple VC Decision Model
      2. 9.2 A Detailed VC Decision Model
      3. 9.3 Modeling Real Decisions
      4. EXERCISES
      5. 9.A Appendix
    4. Chapter 10: Bankruptcy Prediction
      1. 10.1 A Bayesian Network for Predicting Bankruptcy
      2. 10.2 Experiments
      3. EXERCISES
  9. III: Marketing Applications
    1. Chapter 11: Collaborative Filtering
      1. 11.1 Memory-Based Methods
      2. 11.2 Model-Based Methods
      3. 11.3 Experiments
      4. EXERCISES
    2. Chapter 12: Targeted Advertising
      1. 12.1 Class Probability Trees
      2. 12.2 Application to Targeted Advertising
      3. EXERCISES
  10. Bibliography
  11. Index