Bayesian Filtering and Smoothing
2nd Edition
$44.99 ( ) USD
Part of Institute of Mathematical Statistics Textbooks
- Authors:
- Simo Särkkä, Aalto University, Finland
- Lennart Svensson, Chalmers University of Technology, Gothenberg
- Date Published: May 2023
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
- format: Adobe eBook Reader
- isbn: 9781108912303
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Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.
Read more- Contains numerous examples and exercises to demonstrate the uses of the algorithms and how to select the appropriate method for a given purpose
- Features the most comprehensive and up-to-date introduction to non-linear filtering and smoothing in the literature
- Matlab and Python code is available for download, allowing readers to explore how the methods are implemented in practice
- Includes new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering and smoothing by enabling approximations, and posterior linearization filtering and smoothing
Reviews & endorsements
‘The book represents an excellent treatise of non-linear filtering from a Bayesian perspective. It has a nice balance between details and breadth, and it provides a nice journey from the basics of Bayesian inference to sophisticated filtering methods.’ Petar M. Djurić, Stony Brook
See more reviews‘An excellent and pedagogical treatment of the complex world of nonlinear filtering. It is very valuable for both researchers and practitioners.’ Lennart Ljung, Linköping University
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×Product details
- Edition: 2nd Edition
- Date Published: May 2023
- format: Adobe eBook Reader
- isbn: 9781108912303
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
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
Index.-
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Bayesian Filtering and Smoothing
Simo Särkkä, Lennart Svensson
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