Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- 1 Key Points
- 2 Probabilistic Inference
- 3 Gaussian Algebra
- 4 Regression
- 5 Gauss–Markov Processes: Filtering and SDEs
- 6 Hierarchical Inference in Gaussian Models
- 7 Summary of Part I
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
1 - Key Points
from I - Mathematical Background
Published online by Cambridge University Press: 01 June 2022
Book contents
- Frontmatter
- Dedication
- Frontmatter
- Contents
- Acknowledgements
- Symbols and Notation
- Introduction
- I Mathematical Background
- 1 Key Points
- 2 Probabilistic Inference
- 3 Gaussian Algebra
- 4 Regression
- 5 Gauss–Markov Processes: Filtering and SDEs
- 6 Hierarchical Inference in Gaussian Models
- 7 Summary of Part I
- II Integration
- III Linear Algebra
- IV Local Optimisation
- V Global Optimisation
- VI Solving Ordinary Differential Equations
- VII The Frontier
- VIII Solutions to Exercises
- References
- Index
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- Probabilistic NumericsComputation as Machine Learning, pp. 19 - 20Publisher: Cambridge University PressPrint publication year: 2022
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