Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- 8 Key Points
- 9 Introduction
- 10 Bayesian Quadrature
- 11 Links to Classical Quadrature
- 12 Probabilistic Numerical Lessons from Integration
- 13 Summary of Part II and Further Reading
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
13 - Summary of Part II and Further Reading
from II - Integration
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
- 8 Key Points
- 9 Introduction
- 10 Bayesian Quadrature
- 11 Links to Classical Quadrature
- 12 Probabilistic Numerical Lessons from Integration
- 13 Summary of Part II and Further Reading
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
Summary
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- Probabilistic NumericsComputation as Machine Learning, pp. 119 - 122Publisher: Cambridge University PressPrint publication year: 2022