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An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems

Author

Listed:
  • Peiyi Zhang

    (Purdue University)

  • Tianning Dong

    (Purdue University)

  • Faming Liang

    (Purdue University)

Abstract

State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension. The performance of the extended LEnKF algorithm is illustrated by dynamic network embedding and dynamic Poisson spatial models.

Suggested Citation

  • Peiyi Zhang & Tianning Dong & Faming Liang, 2024. "An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems," Computational Statistics, Springer, vol. 39(6), pages 3347-3372, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01443-4
    DOI: 10.1007/s00180-023-01443-4
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/6072 is not listed on IDEAS
    2. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    3. Matthias Katzfuss & Jonathan R. Stroud & Christopher K. Wikle, 2020. "Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 866-885, April.
    4. Daniel K. Sewell & Yuguo Chen, 2015. "Latent Space Models for Dynamic Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1646-1657, December.
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