Sequential Decision Analytics and Modeling

This is a web edition of Sequential Decision Analytics and Modeling (2nd edition), read directly in your browser rather than as a PDF.

Sequential Decision Analytics and Modeling uses a teach-by-example style to illustrate a universal framework for modeling sequential decision problems. The universal framework applies to any sequential decision problem, from active learning problems up through complex resource allocation problems. Chapters are accompanied by Python modules that have implemented the models, but the book should be of value even to people not interested in writing code.

The book is also available as a downloadable PDF, or see the book’s webpage for a fuller overview, the accompanying Python software, and course materials.

Table of contents

• Preface and acknowledgements
• Chapter 1. Modeling sequential decision problems
• Chapter 2. An asset selling problem
• Chapter 3. Adaptive market planning
• Chapter 4. Learning the best diabetes medication
• Chapter 5. Stochastic shortest path problems - Static
• Chapter 6. Stochastic shortest path problems - Dynamic
• Chapter 7. Applications, revisited
• Chapter 8. Energy storage I
• Chapter 9. Energy storage II
• Chapter 10. Supply chain management I: The two-agent newsvendor problem
• Chapter 11. Supply chain management II: The beer game
• Chapter 12. Ad-click optimization
• Chapter 13. Blood management problem
• Chapter 14. Optimizing clinical trials
• References