Bayesian Filtering and Smoothing

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.

• 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.

'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