Bayesian Joint State and Parameter Tracking in Autoregressive Models
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.
Cite
Text
Senoz et al. "Bayesian Joint State and Parameter Tracking in Autoregressive Models." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Senoz et al. "Bayesian Joint State and Parameter Tracking in Autoregressive Models." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/senoz2020l4dc-bayesian/)BibTeX
@inproceedings{senoz2020l4dc-bayesian,
title = {{Bayesian Joint State and Parameter Tracking in Autoregressive Models}},
author = {Senoz, Ismail and Podusenko, Albert and Kouw, Wouter M. and Vries, Bert},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {95-104},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/senoz2020l4dc-bayesian/}
}