The Ensemble Kalman Update is an Empirical Matheron Update
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Abstract (arXiv)
The Ensemble Kalman Filter (EnKF) is a widely used method for data assimilation in high-dimensional systems, with an ensemble update step equivalent to an empirical version of the Matheron update popular in Gaussian process regression -- a connection that links half a century of data-assimilation engineering to modern path-wise GP sampling. This paper provides a compact introduction to this simple but under-exploited connection, with necessary definitions accessible to all fields involved. Source code is available at https://github.com/danmackinlay/paper_matheron_equals_enkf .
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 5 Feb 2025.
Builds on
11 of 30 references.
References · 11
- 2020Efficiently Sampling Functions from Gaussian Process Posteriorscited by 125
- 2020Mat'ern Gaussian processes on Riemannian manifoldscited by 82
- 2020Pathwise Conditioning of Gaussian Processescited by 63
- 2019Coupling techniques for nonlinear ensemble filteringcited by 48
- 2018Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Taskscited by 36
- 2021Auto-differentiable Ensemble Kalman Filterscited by 28
- 2020Online learning of both state and dynamics using ensemble Kalman filterscited by 15
- 2019Convergence Acceleration of Ensemble Kalman Inversion in Nonlinear Settingscited by 9
- 2021Ensemble Inference Methods for Models With Noisy and Expensive Likelihoodscited by 8
- 2024Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systemscited by 2
- 2018Ensemble Kalman Filtering for Online Gaussian Process Regression and Learningcited by 1
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