Abstract
Autoregressive (AR) models are one of the most popular ways to describe different time-varying processes in nature, economics, etc. However, their parameters are often estimated in a batch manner which makes them inefficient for handling large-scale real-time data. In our work, we investigate the feasibility of online parameter estimation for these types of models. We translate the AR model to a probabilistic factor graph which takes advantage of the factorization of the model by implementing inference as a message passing algorithm. Due to the intractability of exact parameter inference for these types of models, sum-product message passing becomes impractical. This suggests to use alternative message passing algorithms based on approximate inference, e.g., variational message passing (VMP) which tries to find variational distributions that serve as good proxies for the exact solution. With VMP, the computations for online state and parameter estimation can be automated.
Original language | English |
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Number of pages | 1 |
Publication status | Published - May 2019 |
Event | 40th WIC Symposium on Information Theory in the Benelux / 9th Joint WIC IEEE SP Symposium on Information Theory and Signal Processing in the Benelux - Gent, Belgium Duration: 28 May 2019 → 29 May 2019 https://agenda.kuleuven.be/en/content/40th-wic-symposium-information-theory-benelux-9th-joint-wic-ieee-sp-symposium-information |
Conference
Conference | 40th WIC Symposium on Information Theory in the Benelux / 9th Joint WIC IEEE SP Symposium on Information Theory and Signal Processing in the Benelux |
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Abbreviated title | SITB2019 |
Country/Territory | Belgium |
City | Gent |
Period | 28/05/19 → 29/05/19 |
Internet address |