Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- II Integration
- III Linear Algebra
- 14 Key Points
- 15 Required Background
- 16 Introduction
- 17 Evaluation Strategies
- 18 A Review of Some Classic Solvers
- 19 Probabilistic Linear Solvers: Algorithmic Scaffold
- 20 Computational Constraints
- 21 Uncertainty Calibration
- 22 Proofs
- 23 Summary of Part III
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
16 - Introduction
from III - Linear Algebra
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
- 14 Key Points
- 15 Required Background
- 16 Introduction
- 17 Evaluation Strategies
- 18 A Review of Some Classic Solvers
- 19 Probabilistic Linear Solvers: Algorithmic Scaffold
- 20 Computational Constraints
- 21 Uncertainty Calibration
- 22 Proofs
- 23 Summary of Part III
- IV Local Optimisation
- V Global Optimisation
- 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. 131 - 136Publisher: Cambridge University PressPrint publication year: 2022