Probabilistic methods for financial and marketing informatics
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 pro...
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| Format: | eBook |
| Language: | Inglés |
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San Fransisco, CA :
Morgan Kaufmann Publishers
c2007.
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| Edition: | 1st edition |
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| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627138906719 |
Table of Contents:
- 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
