Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

• URL: http://arxiv.org/abs/2102.07850v1
• Date: Mon, 15 Feb 2021 21:05:33 GMT
• Title: Differentiable Particle Filtering via Entropy-Regularized Optimal Transport
• Authors: Adrien Corenflos, James Thornton, Arnaud Doucet, George Deligiannidis
• Abstract summary: We introduce a principled differentiable particle filter and provide convergence results. By leveraging optimal transport ideas, we introduce a principled differentiable particle filter and provide convergence results.
• Score: 19.556744028461004
• License: http://creativecommons.org/licenses/by/4.0/
• 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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