Efficient Iterated Filtering

Erik Lindström, Edward Ionides, Jan Frydendall, Henrik Madsen

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-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 publicationIFAC-PapersOnLine (System Identification, Volume 16)
Number of pages6
ISBN (Print)978-3-902823-06-9 (online)
Publication statusPublished - 2012
Event16th IFAC Symposium on System Identification - Brussels, Belgium
Duration: 2012 Jul 112012 Jul 13


Conference16th IFAC Symposium on System Identification

Bibliographical note

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Subject classification (UKÄ)

  • Probability Theory and Statistics

Free keywords

  • Recursive estimation
  • maximum likelihood estimator
  • filtering techniques
  • stochastic approximation
  • iterative methods


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