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Lookup NU author(s): Tom Ryder, Dr Dennis Prangle
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Copyright © 2020 by the author(s)Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process. If such dynamics are non-linear, it can be difficult to estimate the posterior state and forcing term jointly, particularly when there are system parameters that also need estimating. This paper uses black-box variational inference to jointly estimate the posterior, designing a multivariate extension to local inverse autoregressive flows as a flexible approximator of the system. We compare estimates on systems where the posterior is known, demonstrating the effectiveness of the approximation, and apply to problems with non-linear dynamics, multi-output systems and models with non-Gaussian likelihoods.
Author(s): Ward WOC, Ryder T, Prangle D, Alvarez MA
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
Year of Conference: 2020
Pages: 3088-3098
Acceptance date: 02/04/2020
ISSN: 2640-3498
Publisher: ML Research Press
URL: http://proceedings.mlr.press/v108/ward20a.html
Series Title: Proceedings of Machine Learning Research
