Bayesian filtering and smoothing
Author(s)
Bibliographic Information
Bayesian filtering and smoothing
（Institute of Mathematical Statistics textbooks, 17）
Cambridge University Press, 2023
2nd ed
Available at / 3 libraries
-
No Libraries matched.
- Remove all filters.
Note
"First published 2013"--T.p. verso
Includes bibliographical references (p. 379-392) and index
Description and Table of Contents
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.
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.
by "Nielsen BookData"