Ward, W.O.C. orcid.org/0000-0002-4904-7294 and Álvarez, M.A. (Submitted: 2019) Variational bridge constructs for approximate Gaussian process regression. arXiv. (Submitted)
Abstract
This paper introduces a method to approximate Gaussian process regression by representing the problem as a stochastic differential equation and using variational inference to approximate solutions. The approximations are compared with full GP regression and generated paths are demonstrated to be indistinguishable from GP samples. We show that the approach extends easily to non-linear dynamics and discuss extensions to which the approach can be easily applied.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). For reuse permissions, please contact the Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | 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/R034303/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Nov 2020 14:17 |
Last Modified: | 02 Nov 2020 14:26 |
Published Version: | https://arxiv.org/abs/1901.01727v1 |
Status: | Submitted |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167466 |