Probabilistic Methods for Financial and Marketing Informatics (eBook)

Xia Jiang, Richard E. Neapolitan (Autoren)

eBook Download: PDF

2010 | 1. Auflage
432 Seiten
Elsevier Science (Verlag)
978-0-08-055567-6 (ISBN)

Infos

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.

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Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic reasoning to business problems. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors concentrate on showing examples and using the software package Netica to represent and solve problems. The book contains unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. It shares insights about when and why probabilistic methods can and cannot be used effectively. This book is recommended for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes computer science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used are in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science. - 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.

Inhalt

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

Produkt-Details

• Erscheint lt. Verlag: 26.7.2010
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Sozialwissenschaften ► Kommunikation / Medien ► Buchhandel / Bibliothekswesen
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Wirtschaftsinformatik
• ISBN-10: 0-08-055567-5 / 0080555675
• ISBN-13: 978-0-08-055567-6 / 9780080555676