PyDPF：面向可微粒子滤波的 Python 包

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Abstract:State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common to employ particle filtering (PF), an efficient Monte Carlo method for estimating the hidden state corresponding to a sequence of observations. Applying particle filtering requires specifying both the parametric form and the parameters of the system, which are often unknown and must be estimated. Gradient-based optimisation techniques cannot be applied directly to standard particle filters, as the filters themselves are not differentiable. However, several recently proposed methods modify the resampling step to make particle filtering differentiable. In this paper, we present an implementation of several such differentiable particle filters (DPFs) with a unified API built on the popular PyTorch framework. Our implementation makes these algorithms easily accessible to a broader research community and facilitates straightforward comparison between them. We validate our framework by reproducing experiments from several existing studies and demonstrate how DPFs can be applied to address several common challenges with state space modelling.

Comments:	46 pages, 0 figures, under review at the Journal of Statistical Software, the python package can be found at this https URL , the full documentation at this https URL , and the source code including experiment replication material at this https URL
Subjects:	Signal Processing (eess.SP); Machine Learning (cs.LG)
MSC classes:	60-04
ACM classes:	G.3
Cite as:	arXiv:2510.25693 [eess.SP]
	(or arXiv:2510.25693v4 [eess.SP] for this version)
	https://doi.org/10.48550/arXiv.2510.25693 arXiv-issued DOI via DataCite

Submission history

From: John-Joseph Brady [view email]
[v1] Wed, 29 Oct 2025 16:57:54 UTC (86 KB)
[v2] Tue, 4 Nov 2025 15:33:22 UTC (86 KB)
[v3] Mon, 3 Aug 2026 09:42:17 UTC (74 KB)
[v4] Tue, 6 Oct 2026 16:27:46 UTC (89 KB)