Extending Kalman Filters for non-Gaussian Process Noise Sources via Approximate Message Passing
Bibliographic record
Abstract
Estimating time-varying signals becomes particularly challenging in the face of non-Gaussian (e.g., sparse) and rapidly time-varying noise dynamics. By building upon the recent progress in the approximate message passing (AMP) realm, this paper unifies the vector AMP (VAMP) paradigm and the Kalman filter (KF) into a common message passing framework. The new algorithm - coined VAMP-KF - does not restrict the process noise dynamics to a specific structure (e.g., same support over time), thereby accounting for both uncorrelated and correlated noise. Numerical results on rapidly time-varying noise with different sparsity rates and distributions demonstrate unambiguously the effectiveness of the proposed VAMP-KF algorithm and its superiority against state-of-the-art baselines in terms of reconstruction accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".