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2026-03-27

7 saved articles

  3. Using Data Assimilation for Quantitative Electroencephalography Analysis - PubMed

    Lizbeth Peralta-Malváez 1 , · PubMed

    We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of dat …

  4. Ensemble Kalman filter

    Contributors to Wikimedia projects · Wikimedia Foundation, Inc.

    From Wikipedia, the free encyclopedia

  5. High Dimensional Ensemble Kalman Filter

    [Submitted on 1 May 2025 (v1), last revised 17 Sep 2025 (this version, v3)] · arXiv.org

    The Ensemble Kalman Filter (EnKF), as a fundamental data assimilation approach, has been widely used in many fields of the sciences and engineering. When the state variable is of high dimensional accompanied with high resolution observations of physical models, some key theoretical aspects of the EnKF are open for investigation. This paper proposes several high dimensional EnKF (HD-EnKF) methods equipped with consistent estimators for the important forecast error covariance and Kalman Gain matri

  6. Learning Enhanced Ensemble Filters

    [Submitted on 24 Apr 2025 (v1), last revised 23 Dec 2025 (this version, v4)] · arXiv.org

    The filtering distribution in hidden Markov models evolves according to the law of a mean-field model in state-observation space. The ensemble Kalman filter (EnKF) approximates this mean-field model with an ensemble of interacting particles, employing a Gaussian ansatz for the joint distribution of the state and observation at each observation time. These methods are robust, but the Gaussian ansatz limits accuracy. Here this shortcoming is addressed by using machine learning to map the joint pre