Bayesian Filtering and Smoothing
@inproceedings{Srkk2013BayesianFA,
title={Bayesian Filtering and Smoothing},
author={S. S{\"a}rkk{\"a}},
booktitle={Institute of Mathematical Statistics textbooks},
year={2013}
}Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a… Expand
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References
SHOWING 1-10 OF 157 REFERENCES
On sequential Monte Carlo sampling methods for Bayesian filtering
- Mathematics, Computer Science
- Stat. Comput.
- 2000
Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations
- Computer Science, Mathematics
- IEEE Transactions on Signal Processing
- 2010
Robust Filtering and Smoothing with Gaussian Processes
- Mathematics, Computer Science
- IEEE Transactions on Automatic Control
- 2012