Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- 29 Key Points
- 30 Introduction
- 31 Bayesian Optimisation
- 32 Value Loss
- 33 Other Acquisition Functions
- 34 Further Topics
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
34 - Further Topics
from V - Global Optimisation
Published online by Cambridge University Press: 01 June 2022
Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- 29 Key Points
- 30 Introduction
- 31 Bayesian Optimisation
- 32 Value Loss
- 33 Other Acquisition Functions
- 34 Further Topics
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- 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
- Probabilistic NumericsComputation as Machine Learning, pp. 275 - 278Publisher: Cambridge University PressPrint publication year: 2022