Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 gaussian processes
- 3 modeling with gaussian processes
- 4 model assessment, selection, and averaging
- 5 decision theory for optimization
- 6 utility functions for optimization
- 7 common bayesian optimization policies
- 8 computing policies with gaussian processes
- 9 implementation
- 10 theoretical analysis
- 11 extensions and related settings
- 12 a brief history of bayesian optimization
- A the gaussian distribution
- B methods for approximate bayesian inference
- C gradients
- D annotated bibliography of applications
- references
- Index
Index
Published online by Cambridge University Press: 25 January 2023
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 gaussian processes
- 3 modeling with gaussian processes
- 4 model assessment, selection, and averaging
- 5 decision theory for optimization
- 6 utility functions for optimization
- 7 common bayesian optimization policies
- 8 computing policies with gaussian processes
- 9 implementation
- 10 theoretical analysis
- 11 extensions and related settings
- 12 a brief history of bayesian optimization
- A the gaussian distribution
- B methods for approximate bayesian inference
- C gradients
- D annotated bibliography of applications
- references
- Index
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- Bayesian Optimization , pp. 353 - 358Publisher: Cambridge University PressPrint publication year: 2023