Mixture ensemble Kalman filters
A generic algorithmic framework for nonlinear ensemble filtering based on Gaussian mixtures and fuzzy clustering techniques is introduced. The framework generalizes the ensemble Kalman filter and relaxes the assumption of a Gaussian prediction distribution. A theoretical analysis of the proposed procedure is provided, establishing strong consistency under suitable assumptions. Specific implementations are discussed and adjustments that are necessary in high-dimensional settings are proposed. A simple implementation of the filter is shown to work well in common testbeds, providing substantial gains over the ensemble Kalman filter.
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Volume (Year): 58 (2013)
Issue (Month): C ()
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- Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268.
- Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
- Furrer, Reinhard & Bengtsson, Thomas, 2007. "Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 227-255, February.
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