Variational Gaussian filtering via Wasserstein gradient flows

Adrien Corenflos*, Hany Abdulsamad

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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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.

Original languageEnglish
Title of host publication31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PublisherEuropean Signal Processing Conference (EUSIPCO)
Pages1838-1842
Number of pages5
ISBN (Electronic)978-9-4645-9360-0
DOIs
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Helsinki, Finland
Duration: 4 Sept 20238 Sept 2023
Conference number: 31
https://eusipco2023.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Country/TerritoryFinland
CityHelsinki
Period04/09/202308/09/2023
Internet address

Keywords

  • Kalman filtering
  • state-space models
  • variational inference
  • Wasserstein gradient flow

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