A divide and conquer strategy for multinomial particle filter resampling
arXiv:2604.01356v1 Announce Type: new Abstract: This work provides a new multinomial resampling procedure for particle filter resampling, focused on the case where the number of samples required is less than or equal to the size of the underlying discrete distribution. This setting is common in ensemble mixture model filters such as the Gaussian mixture filter. We show superiority of our approach with respect two of the best known multinomial sampling procedures both through a computational complexity analysis and through a numerical experiment.
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Abstract:This work provides a new multinomial resampling procedure for particle filter resampling, focused on the case where the number of samples required is less than or equal to the size of the underlying discrete distribution. This setting is common in ensemble mixture model filters such as the Gaussian mixture filter. We show superiority of our approach with respect two of the best known multinomial sampling procedures both through a computational complexity analysis and through a numerical experiment.
Subjects:
Data Structures and Algorithms (cs.DS); Computation (stat.CO)
Cite as: arXiv:2604.01356 [cs.DS]
(or arXiv:2604.01356v1 [cs.DS] for this version)
https://doi.org/10.48550/arXiv.2604.01356
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Andrey A Popov [view email] [v1] Wed, 1 Apr 2026 20:11:31 UTC (77 KB)
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