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

Peiyi Zhang, Tianning Dong and Faming Liang ()
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Peiyi Zhang: Purdue University
Tianning Dong: Purdue University
Faming Liang: Purdue University

Computational Statistics, 2024, vol. 39, issue 6, No 19, 3347-3372

Abstract: 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.

Keywords: Dynamic network embedding; Ensemble Kalman filter; Sequential Monte Carlo; State space model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01443-4

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