Sparse Gaussian Processes for Stochastic Differential EquationsDownload PDF

Published: 17 Oct 2021, Last Modified: 05 May 2023DLDE Workshop -- NeurIPS 2021 PosterReaders: Everyone
Keywords: stochastic differential equations, variational inference, Gaussian processes, sparse methods
Abstract: We frame the problem of learning stochastic differential equations (SDEs) from noisy observations as an inference problem and aim to maximize the marginal likelihood of the observations in a joint model of the latent paths and the noisy observations. As this problem is intractable, we derive an approximate (variational) inference algorithm and propose a novel parameterization of the approximate distribution over paths using a sparse Markovian Gaussian process. The approximation is efficient in storage and computation, allowing the usage of well-established optimizing algorithms such as natural gradient descent. We demonstrate the capability of the proposed method on the Ornstein-Uhlenbeck process.
Publication Status: This work is unpublished.
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