Ward, W.O.C. orcid.org/0000-0002-4904-7294, Ryder, T., Prangle, D. et al. (1 more author) (2020) Black-box inference for non-linear latent force models. In: Chiappa, S. and Calandra, R., (eds.) International Conference on Artificial Intelligence and Statistics. International Conference on Artificial Intelligence and Statistics, 26-28 Aug 2020, Virtual conference. PMLR - Proceedings of Machine Learning Research , pp. 3088-3098.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2020 The Authors. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > Department of Physics and Astronomy (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/N014162/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Feb 2021 08:44 |
Last Modified: | 03 Feb 2021 08:44 |
Published Version: | http://proceedings.mlr.press/v108/ward20a.html |
Status: | Published |
Publisher: | PMLR - Proceedings of Machine Learning Research |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169438 |