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arXiv · 1906.09199

Black-Box Inference for Non-Linear Latent Force Models

Wil O. C. Ward·Tom Ryder·Dennis Prangle·Mauricio A. Álvarez
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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 approximater 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.

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Wil O. C. Ward, Tom Ryder, Dennis Prangle, Mauricio A. Álvarez. 2019-06-21. Black-Box Inference for Non-Linear Latent Force Models. https://arxiv.org/abs/1906.09199

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