Differentiable Particle Filtering using Optimal Placement Resampling

• URL: http://arxiv.org/abs/2402.16639v1
• Date: Mon, 26 Feb 2024 15:09:56 GMT
• Title: Differentiable Particle Filtering using Optimal Placement Resampling
• Authors: Domonkos Csuzdi, Oliv\'er T\"or\H{o}, Tam\'as B\'ecsi
• Abstract summary: A good proposal distribution and a good resampling scheme are crucial to obtain low variance estimates. This work proposes a differentiable resampling scheme by deterministic sampling from an empirical cumulative distribution function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Particle filters are a frequent choice for inference tasks in nonlinear and non-Gaussian state-space models. They can either be used for state inference by approximating the filtering distribution or for parameter inference by approximating the marginal data (observation) likelihood. A good proposal distribution and a good resampling scheme are crucial to obtain low variance estimates. However, traditional methods like multinomial resampling introduce nondifferentiability in PF-based loss functions for parameter estimation, prohibiting gradient-based learning tasks. This work proposes a differentiable resampling scheme by deterministic sampling from an empirical cumulative distribution function. We evaluate our method on parameter inference tasks and proposal learning.

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