Book contents
- Frontmatter
- Contents
- Preface
- 1 A Data Assimilation Reminder
- 2 Remembrance of Things Path
- 3 SDA Variational Principles
- 4 Using Waveform Information
- 5 Annealing in the Model Precision R f
- 6 Discrete Time Integration in Data Assimilation Variational Principles: Lagrangian and Hamiltonian Formulations
- 7 Monte Carlo Methods
- 8 Machine Learning and Its Equivalence to Statistical Data Assimilation
- 9 Two Examples of the Practical Use of Data Assimilation
- 10 Unfinished Business
- Bibliography
- Index
8 - Machine Learning and Its Equivalence to Statistical Data Assimilation
Published online by Cambridge University Press: 27 January 2022
- Frontmatter
- Contents
- Preface
- 1 A Data Assimilation Reminder
- 2 Remembrance of Things Path
- 3 SDA Variational Principles
- 4 Using Waveform Information
- 5 Annealing in the Model Precision R f
- 6 Discrete Time Integration in Data Assimilation Variational Principles: Lagrangian and Hamiltonian Formulations
- 7 Monte Carlo Methods
- 8 Machine Learning and Its Equivalence to Statistical Data Assimilation
- 9 Two Examples of the Practical Use of Data Assimilation
- 10 Unfinished Business
- Bibliography
- Index
Summary
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- Publisher: Cambridge University PressPrint publication year: 2022