# Variational Gaussian filtering via Wasserstein gradient flows

arXiv: https://arxiv.org/abs/2303.06398 · HTML: https://mlsanity.com/paper/2303.06398/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Variational inference via Wasserstein gradient flows](https://mlsanity.com/paper/2205.15902/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recursive Entropic Variational Inference for Nonlinear State-Space Models](https://mlsanity.com/paper/2511.15409/) (2025)

## Abstract (arXiv)

We present a novel approach to approximate Gaussian and mixture-of-Gaussians filtering. Our method relies on a variational approximation via a gradient-flow representation. The gradient flow is derived from a Kullback--Leibler discrepancy minimization on the space of probability distributions equipped with the Wasserstein metric. We outline the general method and show its competitiveness in posterior representation and parameter estimation on two state-space models for which Gaussian approximations typically fail: systems with multiplicative noise and multi-modal state distributions.