Efficient Iterated Filtering

Erik Lindström, Edward Ionides, Jan Frydendall, Henrik Madsen

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Sammanfattning

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.
Originalspråkengelska
Titel på värdpublikationIFAC-PapersOnLine (System Identification, Volume 16)
FörlagElsevier
Sidor1785-1790
Antal sidor6
ISBN (tryckt)978-3-902823-06-9 (online)
DOI
StatusPublished - 2012
Evenemang16th IFAC Symposium on System Identification - Brussels, Belgien
Varaktighet: 2012 juli 112012 juli 13

Konferens

Konferens16th IFAC Symposium on System Identification
Land/TerritoriumBelgien
OrtBrussels
Period2012/07/112012/07/13

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Ämnesklassifikation (UKÄ)

  • Sannolikhetsteori och statistik

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