Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- Part II Algorithm
- Part III Nonasymptotic Theory
- Part IV Asymptotic Theory
- 13 Asymptotic Learning Theory
- 14 Asymptotic VB Theory of Reduced Rank Regression
- 15 Asymptotic VB Theory of Mixture Models
- 16 Asymptotic VB Theory of Other Latent Variable Models
- 17 Unified Theory for Latent Variable Models
- 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
16 - Asymptotic VB Theory of Other Latent Variable Models
from Part IV - Asymptotic Theory
Published online by Cambridge University Press: 24 June 2019
Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- Part II Algorithm
- Part III Nonasymptotic Theory
- Part IV Asymptotic Theory
- 13 Asymptotic Learning Theory
- 14 Asymptotic VB Theory of Reduced Rank Regression
- 15 Asymptotic VB Theory of Mixture Models
- 16 Asymptotic VB Theory of Other Latent Variable Models
- 17 Unified Theory for Latent Variable Models
- 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
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- Variational Bayesian Learning Theory , pp. 455 - 499Publisher: Cambridge University PressPrint publication year: 2019