Efficient Iterated Filtering

Erik Lindström, Edward Ionides, Jan Frydendall, Henrik Madsen

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationSystem Identification
PublisherInternational Federation of Automatic Control
Publication date2012
ISBN (Print)978-3-902823-06-9
Publication statusPublished - 2012
Event16th IFAC Symposium on System Identification - Square - Brussels Meeting Centre, Brussels, Belgium
Duration: 11 Jul 201213 Jul 2012


Conference16th IFAC Symposium on System Identification
LocationSquare - Brussels Meeting Centre
Internet address
SeriesIFAC Proceedings Volumes (IFAC-PapersOnline)


  • Approximation theory
  • Iterative methods
  • Maximum likelihood estimation
  • Parameter estimation
  • State space methods
  • Estimation

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