
Bayesian Filtering and Smoothing
Cambridge University Press
2nd Edition
Published on 15. June 2023
Book
Paperback/Softback
438 pages
978-1-108-92664-5 (ISBN)
Description
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.
Reviews / Votes
'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. Djuric, Stony Brook 'An excellent and pedagogical treatment of the complex world of nonlinear filtering. It is very valuable for both researchers and practitioners.' Lennart Ljung, Linkoeping UniversityMore details
Series
Edition
2nd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Edition type
Revised edition
Product notice
Paperback (trade)
Illustrations
Worked examples or Exercises
Dimensions
Height: 224 mm
Width: 152 mm
Thickness: 27 mm
Weight
623 gr
ISBN-13
978-1-108-92664-5 (9781108926645)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Simo Saerkkae | Lennart Svensson
Bayesian Filtering and Smoothing
E-Book
05/2023
2nd Edition
Cambridge University Press
€45.99
Available for download
Previous edition

Simo Saerkkae
Bayesian Filtering and Smoothing
Book
09/2013
Cambridge University Press
€105.23
No shipping information available
Persons
Simo Särkkä is Associate Professor in the Department of Electrical Engineering and Automation at Aalto University, Finland. His research interests center on state estimation and stochastic modeling, and he has authored two books (2013 and 2019) on these topics. He is Fellow of ELLIS, Senior Member of IEEE, a recipient of multiple paper awards, and he has been Chair of MLSP and FUSION conferences.
Author
Aalto University, Finland
Chalmers University of Technology, Gothenberg
Content
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.