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Online variational filtering and parameter learning

Abstract:

We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an e...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Christ Church
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
Curran Associates Publisher's website
Host title:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
Volume:
34
Pages:
18633-18645
Publication date:
2022-05-31
Acceptance date:
2021-10-04
Event title:
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Event location:
Virtual event
Event website:
https://nips.cc/Conferences/2021/
Event start date:
2021-12-06
Event end date:
2021-12-14
ISSN:
1049-5258
ISBN:
9781713845393
Language:
English
Keywords:
Pubs id:
1266431
Local pid:
pubs:1266431
Deposit date:
2022-12-08

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