Online variational message passing in the hierarchical Gaussian filter

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2 Citaten (Scopus)

Samenvatting

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.

Originele taal-2Engels
Titel2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings
RedacteurenNelly Pustelnik, Zheng-Hua Tan, Zhanyu Ma, Jan Larsen
Plaats van productiePiscataway
UitgeverijIEEE Computer Society
Aantal pagina's6
Volume2018-September
ISBN van elektronische versie9781538654774
ISBN van geprinte versie978-1-5386-5478-1
DOI's
StatusGepubliceerd - 17 sep 2018
Evenement28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Aalborg, Denemarken
Duur: 17 sep 201820 sep 2018

Congres

Congres28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018
LandDenemarken
StadAalborg
Periode17/09/1820/09/18

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Citeer dit

Senoz, I., & De Vries, B. (2018). Online variational message passing in the hierarchical Gaussian filter. In N. Pustelnik, Z-H. Tan, Z. Ma, & J. Larsen (editors), 2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings (Vol. 2018-September). [8517019] Piscataway: IEEE Computer Society. https://doi.org/10.1109/MLSP.2018.8517019