Elsevier S & T

Probabilistic Methods for Financial and Marketing Informatics

Neapolitan, Richard E.; Jiang, Xia

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Table of contents
  • Contentsvii
  • Prefaceiii
  • Part I: Bayesian Networks and Decision Analysis1
  • Chapter 1. Probabilistic Informatics3
  • 1.1 What Is Informatics?4
  • 1.2 Probabilistic Informatics6
  • 1.3 Outline of This Book7
  • Chapter 2. Probability and Statistics9
  • 2.1 Probability Basics9
  • 2.2 Random Variables16
  • 2.3 The Meaning of Probability24
  • 2.4 Random Variables in Applications30
  • 2.5 Statistical Concepts34
  • Chapter 3. Bayesian Networks53
  • 3.1 What Is a Bayesian Network?54
  • 3.2 Properties of Bayesian Networks56
  • 3.3 Causal Networks as Bayesian Networks63
  • 3.4 Inference in Bayesian Networks72
  • 3.5 How Do We Obtain the Probabilities?78
  • 3.6 Entailed Conditional Independencies *92
  • Chapter 4. Learning Bayesian Networks111
  • 4.1 Parameter Learning112
  • 4.2 Learning Structure (Model Selection)126
  • 4.3 Score-Based Structure Learning *127
  • 4.4 Constraint-Based Structure Learning138
  • 4.5 Causal Learning145
  • 4.6 Software Packages for Learning151
  • 4.7 Examples of Learning153
  • Chapter 5. Decision Analysis Fundamentals177
  • 5.1 Decision Trees178
  • 5.2 Influence Diagrams195
  • 5.3 Dynamic Networks *212
  • Chapter 6. Further Techniques in Decision Analysis229
  • 6.1 Modeling Risk Preferences230
  • 6.2 Analyzing Risk Directly236
  • 6.3 Dominance240
  • 6.4 Sensitivity Analysis244
  • 6.5 Value of Information254
  • 6.6 Normative Decision Analysis259
  • Part II: Financial Applications265
  • Chapter 7. Investment Science267
  • 7.1 Basics of Investment Science267
  • 7.2 Advanced Topics in Investment Science*278
  • 7.3 A Bayesian Network Portfolio Risk Analyzer *314
  • Chapter 8. Modeling Real Options329
  • 8.1 Solving Real Options Decision Problems330
  • 8.2 Making a Plan339
  • 8.3 Sensitivity Analysis340
  • Chapter 9. Venture Capital Decision Making343
  • 9.1 A Simple VC Decision Model345
  • 9.2 A Detailed VC Decision Model347
  • 9.3 Modeling Real Decisions350
  • 9.A Appendix352
  • Chapter 10. Bankruptcy Prediction357
  • 10.1 A Bayesian Network for Predicting Bankruptcy358
  • 10.2 Experiments364
  • Part III: Marketing Applications371
  • Chapter 11. Collaborative Filtering373
  • 11.1 Memory-Based Methods374
  • 11.2 Model-Based Methods377
  • 11.3 Experiments380
  • Chapter 12. Targeted Advertising387
  • 12.1 Class Probability Trees388
  • 12.2 Application to Targeted Advertising390
  • Bibliography397
  • Index409
Book details
  • Vendor Elsevier S & T
  • SKU 9780123704771
  • ISBN-13 9780080555676
  • Author Neapolitan, Richard E.; Jiang, Xia
  • Category Business & Economics
  • Subject Finance

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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.