Efficient Iterated Filtering
(2012) 16th IFAC Symposium on System Identification p.1785-1790- Abstract
- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3008469
- author
- Lindström, Erik LU ; Ionides, Edward ; Frydendall, Jan and Madsen, Henrik
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Recursive estimation, maximum likelihood estimator, filtering techniques, stochastic approximation, iterative methods
- host publication
- IFAC-PapersOnLine (System Identification, Volume 16)
- pages
- 6 pages
- publisher
- Elsevier
- conference name
- 16th IFAC Symposium on System Identification
- conference location
- Brussels, Belgium
- conference dates
- 2012-07-11 - 2012-07-13
- external identifiers
-
- scopus:84867049749
- ISBN
- 978-3-902823-06-9 (online)
- DOI
- 10.3182/20120711-3-BE-2027.00300
- language
- English
- LU publication?
- yes
- additional info
- The paper is accessible (free of charge) to the public . To download the paper an account at IFAC is needed. The web browser Internet Explorer is recommended.
- id
- 497a6486-6683-4929-b12f-27098379cb04 (old id 3008469)
- date added to LUP
- 2016-04-04 12:12:46
- date last changed
- 2022-01-29 23:03:30
@inproceedings{497a6486-6683-4929-b12f-27098379cb04, abstract = {{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.}}, author = {{Lindström, Erik and Ionides, Edward and Frydendall, Jan and Madsen, Henrik}}, booktitle = {{IFAC-PapersOnLine (System Identification, Volume 16)}}, isbn = {{978-3-902823-06-9 (online)}}, keywords = {{Recursive estimation; maximum likelihood estimator; filtering techniques; stochastic approximation; iterative methods}}, language = {{eng}}, pages = {{1785--1790}}, publisher = {{Elsevier}}, title = {{Efficient Iterated Filtering}}, url = {{http://dx.doi.org/10.3182/20120711-3-BE-2027.00300}}, doi = {{10.3182/20120711-3-BE-2027.00300}}, year = {{2012}}, }