Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- 1 Bayesian Learning
- 2 Variational Bayesian Learning
- Part II Algorithm
- Part III Nonasymptotic Theory
- Part IV Asymptotic Theory
- Appendix A James–Stein Estimator
- Appendix B Metric in Parameter Space
- Appendix C Detailed Description of Overlap Method
- Appendix D Optimality of Bayesian Learning
- Bibliography
- Index
2 - Variational Bayesian Learning
from Part I - Formulation
Published online by Cambridge University Press: 24 June 2019
Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- 1 Bayesian Learning
- 2 Variational Bayesian Learning
- Part II Algorithm
- Part III Nonasymptotic Theory
- Part IV Asymptotic Theory
- Appendix A James–Stein Estimator
- Appendix B Metric in Parameter Space
- Appendix C Detailed Description of Overlap Method
- Appendix D Optimality of Bayesian Learning
- Bibliography
- Index
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- Variational Bayesian Learning Theory , pp. 39 - 60Publisher: Cambridge University PressPrint publication year: 2019