Optimized Bayesian Dynamic Advising

Theory and Algorithms

ISBN: 978-1-85233-928-9 (Print) 978-1-84628-254-6 (Online)

Table of contents (15 chapters)

  1. Front Matter

    Pages I-XV

  2. No Access

    Chapter

    Pages 1-10

    Introduction

  3. No Access

    Chapter

    Pages 11-41

    Underlying theory

  4. No Access

    Chapter

    Pages 43-56

    Approximate and feasible learning

  5. No Access

    Chapter

    Pages 57-65

    Approximate design

  6. No Access

    Chapter

    Pages 67-94

    Problem formulation

  7. No Access

    Chapter

    Pages 95-192

    Solution and principles of its approximation: learning part

  8. No Access

    Chapter

    Pages 193-241

    Solution and principles of its approximation: design part

  9. No Access

    Chapter

    Pages 243-308

    Learning with normal factors and components

  10. No Access

    Chapter

    Pages 309-376

    Design with normal mixtures

  11. No Access

    Chapter

    Pages 377-410

    Learning with Markov-chain factors and components

  12. No Access

    Chapter

    Pages 411-435

    Design with Markov-chain mixtures

  13. No Access

    Chapter

    Pages 437-461

    Sandwich BMTB for mixture initiation

  14. No Access

    Chapter

    Pages 463-479

    Mixed mixtures

  15. No Access

    Chapter

    Pages 481-506

    Applications of the advisory system

  16. No Access

    Chapter

    Pages 507-509

    Concluding remarks

  17. Back Matter

    Pages 511-529