Bayesian joint state and parameter tracking in autoregressive models

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Abstract

We address the problem of online Bayesian state and parameter tracking in autoregressive (AR) models with time-varying process noise variance. The involved marginalization and expectation integrals cannot be analytically solved. Moreover, the online tracking constraint makes sampling and batch learning methods unsuitable for this problem. We propose a hybrid variational message passing algorithm that robustly tracks the time-varying dynamics of the latent states, AR coefficients and process noise variance. Since message passing in a factor graph is a highly modular inference approach, the proposed methods easily extend to other non-stationary dynamic modeling problems.
Original languageEnglish
Title of host publicationConference on Learning for Dynamics and Control
PublisherProceedings of Machine Learning Research
Number of pages10
Publication statusPublished - Jun 2020
Event2nd Annual Conference on Learning for Dynamics and Control - UC Berkeley, Berkeley, United States
Duration: 11 Jun 202012 Jun 2020
Conference number: 2
https://l4dc.org

Conference

Conference2nd Annual Conference on Learning for Dynamics and Control
Abbreviated titleL4DC
CountryUnited States
CityBerkeley
Period11/06/2012/06/20
Internet address

Keywords

  • Autoregressive models
  • hierarchical Gaussian filter
  • factor graphs
  • online learning
  • variational message passing

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  • Cite this

    Senoz, I., Podusenko, A., Kouw, W. M., & de Vries, A. B. (2020). Bayesian joint state and parameter tracking in autoregressive models. In Conference on Learning for Dynamics and Control [120] Proceedings of Machine Learning Research. https://openreview.net/pdf?id=uaYWeSAp5a5