Probabilistic methods for financial and marketing informatics

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 us...

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Main Authors Neapolitan, Richard E., Jiang, Xia
Format eBook Book
LanguageEnglish
Published San Francisco, Calif Elsevier 2007
Oxford Morgan Kaufmann
Elsevier Science & Technology
Morgan Kaufmann Publ
Edition1
Subjects
Online AccessGet full text
ISBN0123704774
9780123704771
DOI10.1016/B978-0-12-370477-1.X5016-6

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Abstract 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 financeShares insights about when and why probabilistic methods can and cannot be used effectivelyComplete review of Bayesian networks and probabilistic methods for those IT professionals new to informatics.
AbstractList 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.
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 financeShares insights about when and why probabilistic methods can and cannot be used effectivelyComplete review of Bayesian networks and probabilistic methods for those IT professionals new to 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 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.
Author Neapolitan, Richard E.
Jiang, Xia
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Snippet Probabilistic Methods for Financial and Marketing Informatics aims to provide students with insights and a guide explaining how to apply probabilistic...
Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas, from medicine to image...
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SubjectTerms Bayes-Statistik
Bayesian method
Bayesian statistical decision theory
Bayesian statistical decision theory -- Data processing
Data processing
Decision theory
Entscheidungstheorie
Finance
Finance -- Statistical methods
Information technology
Marketing
Marketing - Statistical methods
Portfolio-Management
Realoptionsansatz
Statistical methods
Statistische Verteilung
Theorie
Wahrscheinlichkeitsrechnung
TableOfContents 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
Title Probabilistic methods for financial and marketing informatics
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