Book contents
- Frontmatter
- Contents
- Preface
- Symbols and Abbreviations
- 1 What Are Bayesian Filtering and Smoothing?
- 2 Bayesian Inference
- 3 Batch and Recursive Bayesian Estimation
- 4 Discretization of Continuous-Time Dynamic Models
- 5 Modeling with State Space Models
- 6 Bayesian Filtering Equations and Exact Solutions
- 7 Extended Kalman Filtering
- 8 General Gaussian Filtering
- 9 Gaussian Filtering by Enabling Approximations
- 10 Posterior Linearization Filtering
- 11 Particle Filtering
- 12 Bayesian Smoothing Equations and Exact Solutions
- 13 Extended Rauch–Tung–Striebel Smoothing
- 14 General Gaussian Smoothing
- 15 Particle Smoothing
- 16 Parameter Estimation
- 17 Epilogue
- Appendix Additional Material
- References
- List of Examples
- List of Theorems, Corollaries, and Algorithms
- Index
8 - General Gaussian Filtering
Published online by Cambridge University Press: 01 June 2023
Book contents
- Frontmatter
- Contents
- Preface
- Symbols and Abbreviations
- 1 What Are Bayesian Filtering and Smoothing?
- 2 Bayesian Inference
- 3 Batch and Recursive Bayesian Estimation
- 4 Discretization of Continuous-Time Dynamic Models
- 5 Modeling with State Space Models
- 6 Bayesian Filtering Equations and Exact Solutions
- 7 Extended Kalman Filtering
- 8 General Gaussian Filtering
- 9 Gaussian Filtering by Enabling Approximations
- 10 Posterior Linearization Filtering
- 11 Particle Filtering
- 12 Bayesian Smoothing Equations and Exact Solutions
- 13 Extended Rauch–Tung–Striebel Smoothing
- 14 General Gaussian Smoothing
- 15 Particle Smoothing
- 16 Parameter Estimation
- 17 Epilogue
- Appendix Additional Material
- References
- List of Examples
- List of Theorems, Corollaries, and Algorithms
- Index
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- Bayesian Filtering and Smoothing , pp. 131 - 167Publisher: Cambridge University PressPrint publication year: 2023