Transmissibility-based Kalman Filtering For Systems With Non-Gaussian Process Noise
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Bibliographic record
Abstract
The concept of transmissibility operators refers to the mathematical relationships between system outputs. They can be used to estimate the independent output of a system based on sensor measurements only. In this case, the output estimation is independent of the process noise or unmodeled dynamics. This allows for the estimation of process noise regardless of its probability distribution. The proposed approach takes into account the possibility of using the Kalman filter theme in the filtering of output noise regardless of the process noise distribution. The proposed approach does not require the covariance estimation of the process noise. Since the proposed approach considers the ability to formulate unmodeled dynamics or parameter uncertainties as non-Gaussian process noise, it can handle both. The potential of this approach is demonstrated by implementing it in a group of connected autonomous robots.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it