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
- VI Solving Ordinary Differential Equations
- 35 Key Points
- 36 Introduction
- 37 Classical ODE Solvers as Regression Methods
- 38 ODE Filters and Smoothers
- 39 Theory of ODE Filters and Smoothers
- 40 Perturbative Solvers
- 41 Further Topics
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
41 - Further Topics
from VI - Solving Ordinary Differential Equations
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
- VI Solving Ordinary Differential Equations
- 35 Key Points
- 36 Introduction
- 37 Classical ODE Solvers as Regression Methods
- 38 ODE Filters and Smoothers
- 39 Theory of ODE Filters and Smoothers
- 40 Perturbative Solvers
- 41 Further Topics
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
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- Probabilistic NumericsComputation as Machine Learning, pp. 339 - 348Publisher: Cambridge University PressPrint publication year: 2022