# An Adaptive Covariance Parameterization Technique for the Ensemble Gaussian Mixture Filter

arXiv: https://arxiv.org/abs/2212.10323 · HTML: https://mlsanity.com/paper/2212.10323/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [The Ensemble Epanechnikov Mixture Filter](https://mlsanity.com/paper/2408.11164/) (2024)
- [Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach](https://mlsanity.com/paper/2605.01089/) (2026)

## Abstract (arXiv)

The ensemble Gaussian mixture filter combines the simplicity and power of Gaussian mixture models with the provable convergence and power of particle filters. The quality of the ensemble Gaussian mixture filter heavily depends on the choice of covariance matrix in each Gaussian mixture. This work extends the ensemble Gaussian mixture filter to an adaptive choice of covariance based on the parameterized estimates of the sample covariance matrix. Through the use of the expectation maximization algorithm, optimal choices of the covariance matrix parameters are computed in an online fashion. Numerical experiments on the Lorenz '63 equations show that the proposed methodology converges to classical results known in particle filtering. Further numerical results with more advances choices of covariance parameterization and the medium-size Lorenz '96 equations show that the proposed approach can perform significantly better than the standard EnGMF, and other classical data assimilation algorithms.