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· 1999 · arXiv 1999.772544

1 Pith paper cite this work. Polarity classification is still indexing.

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fields

cs.LG 1

years

2026 1

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UNVERDICTED 1

representative citing papers

Generative Model Proposal based Particle Filtering for Data Assimilation

cs.LG · 2026-07-01 · unverdicted · novelty 6.0

FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.

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Showing 1 of 1 citing paper.

  • Generative Model Proposal based Particle Filtering for Data Assimilation cs.LG · 2026-07-01 · unverdicted · none · ref 29

    FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.