Variational Gaussian filtering via Wasserstein gradient flows
Computation
2023-06-21 v2 Computational Engineering, Finance, and Science
Systems and Control
Systems and Control
Machine Learning
Abstract
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.
Cite
@article{arxiv.2303.06398,
title = {Variational Gaussian filtering via Wasserstein gradient flows},
author = {Adrien Corenflos and Hany Abdulsamad},
journal= {arXiv preprint arXiv:2303.06398},
year = {2023}
}
Comments
5 pages, 2 figures, double column, minor modifications compared to version 1 (more experiments + typos). Accepted as a conference paper to EUSIPCO 2023