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Mixture ensemble Kalman filters

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  • Frei, Marco
  • Künsch, Hans R.
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    Abstract

    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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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 127-138

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    Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:127-138

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Nonlinear filtering; Data assimilation; Ensemble Kalman filter; Fuzzy clustering; Gaussian mixtures;

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