Front Cover -- Probabilistic Methods for Financial and Marketing Informatics -- Copyright Page -- Preface -- Contents -- Part I: Bayesian Networks and Decision Analysis -- Chapter 1. Probabilistic Informatics -- 1.1 What Is Informatics? -- 1.2 Probabilistic Informatics -- 1.3 Outline of This Book -- Chapter 2. Probability and Statistics -- 2.1 Probability Basics -- 2.2 Random Variables -- 2.3 The Meaning of Probability -- 2.4 Random Variables in Applications -- 2.5 Statistical Concepts -- Chapter 3. Bayesian Networks -- 3.1 What Is a Bayesian Network? -- 3.2 Properties of Bayesian Networks -- 3.3 Causal Networks as Bayesian Networks -- 3.4 Inference in Bayesian Networks -- 3.5 How Do We Obtain the Probabilities? -- 3.6 Entailed Conditional Independencies * -- Chapter 4. Learning Bayesian Networks -- 4.1 Parameter Learning -- 4.2 Learning Structure (Model Selection) -- 4.3 Score-Based Structure Learning * -- 4.4 Constraint-Based Structure Learning -- 4.5 Causal Learning -- 4.6 Software Packages for Learning -- 4.7 Examples of Learning -- Chapter 5. Decision Analysis Fundamentals -- 5.1 Decision Trees -- 5.2 Influence Diagrams -- 5.3 Dynamic Networks * -- Chapter 6. Further Techniques in Decision Analysis -- 6.1 Modeling Risk Preferences -- 6.2 Analyzing Risk Directly -- 6.3 Dominance -- 6.4 Sensitivity Analysis -- 6.5 Value of Information -- 6.6 Normative Decision Analysis -- Part II: Financial Applications -- Chapter 7. Investment Science -- 7.1 Basics of Investment Science -- 7.2 Advanced Topics in Investment Science* -- 7.3 A Bayesian Network Portfolio Risk Analyzer * -- Chapter 8. Modeling Real Options -- 8.1 Solving Real Options Decision Problems -- 8.2 Making a Plan -- 8.3 Sensitivity Analysis -- Chapter 9. Venture Capital Decision Making -- 9.1 A Simple VC Decision Model -- 9.2 A Detailed VC Decision Model -- 9.3 Modeling Real Decisions.

9.A Appendix -- Chapter 10. Bankruptcy Prediction -- 10.1 A Bayesian Network for Predicting Bankruptcy -- 10.2 Experiments -- Part III: Marketing Applications -- Chapter 11. Collaborative Filtering -- 11.1 Memory-Based Methods -- 11.2 Model-Based Methods -- 11.3 Experiments -- Chapter 12. Targeted Advertising -- 12.1 Class Probability Trees -- 12.2 Application to Targeted Advertising -- Bibliography -- Index.