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Title resolution pending

· 1994 · DOI 10.1002/qj.49712051912

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

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fields

cs.LG 1

years

2026 1

verdicts

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 25

    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.