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
29 - Key Points
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
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- Probabilistic NumericsComputation as Machine Learning, pp. 245 - 246Publisher: Cambridge University PressPrint publication year: 2022