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Cover: 9781108926645 | Bayesian Filtering and Smoothing | Simo Särkkä (u. a.) | Taschenbuch
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Bayesian Filtering and Smoothing
Taschenbuch von Simo Särkkä (u. a.)
Sprache: Englisch

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Beschreibung
"Now in its second edition, this accessible text presents a unified Bayesian treatment of the 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 the Matlab and Python code available online, enabling readers to implement the algorithms in their own projects"--
"Now in its second edition, this accessible text presents a unified Bayesian treatment of the 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 the Matlab and Python code available online, enabling readers to implement the algorithms in their own projects"--
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2023
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 430
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108926645
ISBN-10: 1108926649
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Särkkä, Simo
Svensson, Lennart
Auflage: 2nd edition
Hersteller: Cambridge University Press
Maße: 224 x 152 x 27 mm
Von/Mit: Simo Särkkä (u. a.)
Erscheinungsdatum: 15.06.2023
Gewicht: 0,623 kg
preigu-id: 126328751
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2023
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 430
Inhalt: Kartoniert / Broschiert
ISBN-13: 9781108926645
ISBN-10: 1108926649
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Särkkä, Simo
Svensson, Lennart
Auflage: 2nd edition
Hersteller: Cambridge University Press
Maße: 224 x 152 x 27 mm
Von/Mit: Simo Särkkä (u. a.)
Erscheinungsdatum: 15.06.2023
Gewicht: 0,623 kg
preigu-id: 126328751
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