Online variational message passing in the hierarchical Gaussian filter

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Abstract

We address the problem of online state and parameter estimation in hierarchical Bayesian nonlinear dynamic systems. We focus on the Hierarchical Gaussian Filter (HGF), which is a popular model in the computational neuroscience literature. For this filter, explicit equations for online state estimation (and offline parameter estimation) have been derived before. We extend this work by casting the HGF as a probabilistic factor graph and present variational message passing update rules that facilitate both online state and parameter estimation as well as online tracking of the free energy (or ELBO), which can be used as a proxy for Bayesian evidence. Due to the locality and modularity of the factor graph framework, our approach supports application of HGF's and variations as plug-in modules to a wide variety of dynamic modelling applications.

Original languageEnglish
Title of host publication2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
EditorsNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Place of PublicationPiscataway
PublisherIEEE Computer Society
Number of pages6
Volume2018-September
ISBN (Electronic)9781538654774
ISBN (Print)978-1-5386-5478-1
DOIs
Publication statusPublished - 17 Sep 2018
Event28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denmark
Duration: 17 Sep 201820 Sep 2018

Conference

Conference28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
CountryDenmark
CityAalborg
Period17/09/1820/09/18

Keywords

  • Dynamical systems
  • Free energy
  • Hierarchical Gaussian Filter
  • Online state and parameter estimation
  • Variational Message Passing

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