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arXiv · 2103.00988

Moment-Based Variational Inference for Stochastic Differential Equations

Christian Wildner·Heinz Koeppl
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Abstract

Existing deterministic variational inference approaches for diffusion processes use simple proposals and target the marginal density of the posterior. We construct the variational process as a controlled version of the prior process and approximate the posterior by a set of moment functions. In combination with moment closure, the smoothing problem is reduced to a deterministic optimal control problem. Exploiting the path-wise Fisher information, we propose an optimization procedure that corresponds to a natural gradient descent in the variational parameters. Our approach allows for richer variational approximations that extend to state-dependent diffusion terms. The classical Gaussian process approximation is recovered as a special case.

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BibTeXRIS

Christian Wildner, Heinz Koeppl. 2021-03-01. Moment-Based Variational Inference for Stochastic Differential Equations. https://arxiv.org/abs/2103.00988

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