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arXiv · 2402.16639

Differentiable Particle Filtering using Optimal Placement Resampling

Domonkos Csuzdi·Olivér Törő·Tamás Bécsi
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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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Domonkos Csuzdi, Olivér Törő, Tamás Bécsi. 2024-02-26. Differentiable Particle Filtering using Optimal Placement Resampling. https://doi.org/10.1109/saci60582.2024.10619755

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