O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Probabilistic Methods for Financial and Marketing Informatics

Book Description

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.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. About the Authors
  7. I: Bayesian Networks and Decision Analysis
    1. Chapter 1: Probabilistic Informatics
      1. 1.1 What Is Informatics?
      2. 1.2 Probabilistic Informatics
      3. 1.3 Outline of This Book
    2. Chapter 2: Probability and Statistics
      1. 2.1 Probability Basics
      2. 2.2 Random Variables
      3. 2.3 The Meaning of Probability
      4. 2.4 Random Variables in Applications
      5. 2.5 Statistical Concepts
      6. EXERCISES
    3. Chapter 3: Bayesian Networks
      1. 3.1 What Is a Bayesian Network?
      2. 3.2 Properties of Bayesian Networks
      3. 3.3 Causal Networks as Bayesian Networks
      4. 3.4 Inference in Bayesian Networks
      5. 3.5 How Do We Obtain the Probabilities?
      6. 3.6 Entailed Conditional Independencies*
      7. EXERCISES
      8. Section 3.1
      9. Section 3.2
      10. Section 3.3
      11. Section 3.4
      12. Section 3.5
    4. Chapter 4: Learning Bayesian Networks
      1. 4.1 Parameter Learning
      2. 4.2 Learning Structure (Model Selection)
      3. 4.3 Score-Based Structure Learning*
      4. 4.4 Constraint-Based Structure Learning
      5. 4.5 Causal Learning
      6. 4.6 Software Packages for Learning
      7. 4.7 Examples of Learning
      8. EXERCISES
      9. Section 4.1
      10. Section 4.3
      11. Section 4.4
      12. Section 4.5
      13. Section 4.6
      14. Section 4.7
    5. Chapter 5: Decision Analysis Fundamentals
      1. 5.1 Decision Trees
      2. 5.2 Influence Diagrams
      3. 5.3 Dynamic Networks*
      4. EXERCISES
      5. Section 5.1
      6. Section 5.2
      7. Section 5.3
    6. Chapter 6: Further Techniques in Decision Analysis
      1. 6.1 Modeling Risk Preferences
      2. 6.2 Analyzing Risk Directly
      3. 6.3 Dominance
      4. 6.4 Sensitivity Analysis
      5. 6.5 Value of Information
      6. 6.6 Normative Decision Analysis
      7. EXERCISES
      8. Section 6.1
      9. Section 6.2
      10. Section 6.3
      11. Section 6.4
      12. Section 6.5
  8. II: Financial Applications
    1. Chapter 7: Investment Science
      1. 7.1 Basics of Investment Science
      2. 7.2 Advanced Topics in Investment Science*
      3. 7.3 A Bayesian Network Portfolio Risk Analyzer*
      4. EXERCISES
      5. Section 7.1
      6. Section 7.2
      7. Section 7.3
    2. Chapter 8: Modeling Real Options
      1. 8.1 Solving Real Options Decision Problems
      2. 8.2 Making a Plan
      3. 8.3 Sensitivity Analysis
      4. EXERCISES
      5. Section 8.1
      6. Section 8.2
      7. Section 8.3
    3. Chapter 9: Venture Capital Decision Making
      1. 9.1 A Simple VC Decision Model
      2. 9.2 A Detailed VC Decision Model
      3. 9.3 Modeling Real Decisions
      4. EXERCISES
      5. 9.A Appendix
    4. Chapter 10: Bankruptcy Prediction
      1. 10.1 A Bayesian Network for Predicting Bankruptcy
      2. 10.2 Experiments
      3. EXERCISES
  9. III: Marketing Applications
    1. Chapter 11: Collaborative Filtering
      1. 11.1 Memory-Based Methods
      2. 11.2 Model-Based Methods
      3. 11.3 Experiments
      4. EXERCISES
    2. Chapter 12: Targeted Advertising
      1. 12.1 Class Probability Trees
      2. 12.2 Application to Targeted Advertising
      3. EXERCISES
  10. Bibliography
  11. Index