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Y. Tarumi

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#machine learning Preprint Sep 2026

PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

PR-Smoother is introduced, a simulator-preserving amortized smoother designed for this prescribed-simulator DA regime that yields an explicit non-Gaussian smoothing distribution over physical trajectories and supports joint state, parameter, and sensor-bias learning from observations alone.

Y. Tarumi · 0 citations