Conference paper · Journal article
Efficient Iterated Filtering
Department of Informatics and Mathematical Modeling, Technical University of Denmark1
Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark2
CERE – Center for Energy Ressources Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark3
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark4
Parameter estimation in general state space models is not trivial as the likelihood is unknown. We propose a recursive estimator for general state space models, and show that the estimates converge to the true parameters with probability one. The estimates are also asymptotically Cramer-Rao efficient.
The proposed estimator is easy to implement as it only relies on non-linear filtering. This makes the framework flexible as it is easy to tune the implementation to achieve computational efficiency. This is done by using the approximation of the score function derived from the theory on Iterative Filtering as a building block within the recursive maximum likelihood estimator.
Language: | English |
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Publisher: | International Federation of Automatic Control |
Year: | 2012 |
Pages: | 1785-1790 |
Proceedings: | 16th IFAC Symposium on System Identification |
Series: | Ifac Proceedings Volumes (ifac-papersonline) |
ISBN: | 3902823062 and 9783902823069 |
ISSN: | 14746670 |
Types: | Conference paper and Journal article |
DOI: | 10.3182/20120711-3-BE-2027.00300 |
ORCIDs: | Madsen, Henrik |