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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.03048 |
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| _version_ | 1866916955333591040 |
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| author | MacKinlay, Dan |
| author_facet | MacKinlay, Dan |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03048 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Ensemble Kalman Update is an Empirical Matheron Update MacKinlay, Dan Machine Learning 93E11 (Primary) 65C20 62M20 (Secondary) 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 . |
| title | The Ensemble Kalman Update is an Empirical Matheron Update |
| topic | Machine Learning 93E11 (Primary) 65C20 62M20 (Secondary) |
| url | https://arxiv.org/abs/2502.03048 |