| Title | Efficient Iterated Filtering |
| Authors | Erik Lindström, Edward Ionides, Jan Frydendall, Henrik Madsen |
| Alternative Location | http://dx.doi.org/10.3182/2... |
| Publication | IFAC-PapersOnLine (System Identification, Volume 16) |
| Year | 2012 |
| Pages | 1785 - 1790 |
| Document type | Conference paper |
| Conference name | 16th IFAC Symposium on System Identification |
| Conference Date | 2012-07-11/2012-07-13 |
| Conference Location | Brussels, Belgium |
| Status | Published |
| Quality controlled | Yes |
| Language | eng |
| Publisher | IFAC & Elsevier Ltd. |
| Abstract English | 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. |
| Keywords | Recursive estimation, maximum likelihood estimator, filtering techniques, stochastic approximation, iterative methods, |
| ISBN/ISSN/Other | ISBN: 978-3-902823-06-9 (online) |
Questions: webmaster
Last update: 2013-04-11
Centre for Mathematical Sciences, Box 118, SE-22100, Lund. Telefon: +46 46-222 00 00 (vx)