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arXiv · 2102.07850

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Adrien Corenflos·James Thornton·George Deligiannidis·Arnaud Doucet
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Abstract

Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF, necessary to obtain low variance likelihood and states estimates. However, traditional resampling methods result in PF-based loss functions being non-differentiable with respect to model and PF parameters. In a variational inference context, resampling also yields high variance gradient estimates of the PF-based evidence lower bound. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results. We demonstrate this novel method on a variety of applications.

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Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet. 2021-02-15. Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. https://arxiv.org/abs/2102.07850

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