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Black-Box Inference for Non-Linear Latent Force Models

Lookup NU author(s): Tom Ryder, Dr Dennis Prangle

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Abstract

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.

Publication metadata

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

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