PSEF trains a permutation-invariant transformer analysis map on synthetic state-observation pairs using strictly proper scoring rules to approximate the true Bayesian filter, with a proof under realizability.
Pierre Del Moral, Arnaud Doucet, and Ajay Jasra
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
citing papers explorer
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Learning Probabilistic Filters with Strictly Proper Scoring Rules
PSEF trains a permutation-invariant transformer analysis map on synthetic state-observation pairs using strictly proper scoring rules to approximate the true Bayesian filter, with a proof under realizability.
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Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.