pith:6W6U7QBC
Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions
A measurement-aware forward process built from the measurement equation yields exact likelihood scores for score-based data assimilation.
arxiv:2604.02889 v2 · 2026-04-03 · stat.ML · cs.AI · cs.LG
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Claims
we propose a measurement-aware score-based filter (MASF) that defines a measurement-aware forward process directly from the measurement equation. This construction makes the likelihood score analytically tractable: for linear measurements, we derive the exact likelihood score and combine it with a learned prior score to obtain the posterior score.
That constructing the forward process directly from the measurement equation preserves the diffusion properties needed for stable score-based sampling and does not introduce new instabilities or approximation errors in high dimensions.
A measurement-aware forward process for score-based data assimilation yields an exact likelihood score for linear measurements by construction.
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| First computed | 2026-05-22T01:04:01.398360Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Canonical record JSON
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