A diffusion score matching based Kalman filter is developed for robust ensemble filtering under observation noise misspecification, with theoretical guarantees and ensemble implementations tested on Lorenz systems.
Bounds on the jensen gap, and implications for mean-concentrated distributions
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Diffusion models solve noisy (non)linear inverse problems via approximated posterior sampling that blends diffusion steps with manifold gradients without strict consistency projection.
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Robust ensemble Kalman filtering under observation noise misspecification via diffusion score matching
A diffusion score matching based Kalman filter is developed for robust ensemble filtering under observation noise misspecification, with theoretical guarantees and ensemble implementations tested on Lorenz systems.
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Diffusion Posterior Sampling for General Noisy Inverse Problems
Diffusion models solve noisy (non)linear inverse problems via approximated posterior sampling that blends diffusion steps with manifold gradients without strict consistency projection.