Bayesian Filtering and Smoothing
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.
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Bayesian Filtering and Smoothing
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.
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Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing

Overview

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.

Product Details

ISBN-13: 9781108926645
Publisher: Cambridge University Press
Publication date: 06/15/2023
Series: Institute of Mathematical Statistics Textbooks , #17
Edition description: 2nd ed.
Pages: 438
Product dimensions: 5.98(w) x 8.98(h) x 0.91(d)

About the Author

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.

Lennart Svensson is Professor in the Department of Electrical Engineering at Chalmers University of Technology, Gothenberg. His research focuses on nonlinear filtering, deep learning, and tracking in particular. He has organized a massive open online course on multiple object tracking, and received paper awards at the International Conference on Information Fusion in 2009, 2010, 2017, 2019, and 2021.

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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