Differentiable Particle Filtering using Optimal Placement Resampling
<|reference_start|>Differentiable Particle Filtering using Optimal Placement Resampling: 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.<|reference_end|>
@article{csuzdi2024differentiable,
    title={Differentiable Particle Filtering using Optimal Placement Resampling},
    author={Domonkos Csuzdi, Oliv'er T"orH{o}, Tam'as B'ecsi},
    journal={arXiv preprint arXiv:2402.16639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16639},
    primaryClass={cs.LG stat.CO}
}