Probabilistic methods for financial and marketing informatics [[electronic resource] /] / Richard E. Neapolitan, Xia Jiang

• Autore: Neapolitan Richard E
• Pubblicazione: San Fransisco, CA, : Morgan Kaufmann Publishers, c2007
• Edizione: 1st edition
• Descrizione fisica: 1 online resource (427 p.)
• Disciplina: 332.01/519542
• Soggetto topico: Finance - Statistical methods; Bayesian statistical decision theory - Data processing; Marketing - Statistical methods; Information technology
• Altri autori: JiangXia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 397-408) and index.
• Nota di contenuto: 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
• Sommario/riassunto: 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 informati
• Titolo autorizzato: Probabilistic methods for financial and marketing informatics
• ISBN: 1-281-31147-2; 9786611311476; 0-08-055567-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910784616403321
• Lo trovi qui: Univ. Federico II