The second edition of this accessible introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. The book introduces all the main concepts and ideas, and contains numerous examples and exercises to let you put the theory into practice.
The second edition of this accessible introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. The book introduces all the main concepts and ideas, and contains numerous examples and exercises to let you put the theory into practice.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.