Distributed Student's t filtering algorithm for heavy‐tailed noises
Bibliographic record
Abstract
Summary In this paper, a distributed Student's t filtering algorithm to deal with heavy‐tailed noises is developed. In the traditional Kalman filter, the distribution of the signal is assumed. However, in reality, outliers in the signal are often encountered for which the assumption of Gaussian distribution is no longer valid. The Student's t distribution can describe noises in the presence of outliers. As a result, the weight on each data point within the filter adapts to the data quality so that the filter becomes insensitive to the outliers. We first derive the distributed filtering algorithm from the centralized Student's t filter, which is able to handle heavy‐tailed noises such as outliers and then analyze properties of the proposed method. It is shown that the proposed algorithm provides the same accuracy as the centralized Student's t filtering with no performance loss. Furthermore, the distributed Student's t filtering with feedback is developed, which is in accordance with centralized filtering, and the local error covariance is reduced as expected. Two numerical examples support the theoretical results and illustrate the validity of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".