Abnormality Detection with Rényi Divergence for Univariate Gaussian Data
Bibliographic record
Abstract
This paper investigates the abnormality detection based on the Rényi divergence with the divergence order between 0 and 1. It is assumed that univariate Gaussian data samples are independent and identically distributed (i.i.d.). The distance between the estimated distribution from observed data and that from historical data under the normal condition is quantified by the Rényi divergence, the proposed test statistic. The false alarm rate (FAR) and the missed alarm rate (MAR) are derived analytically based on the distribution of the Rényi divergence. Under the abnormal condition, constant bias and multiplicative faults are considered. Taking both the FAR and MAR into consideration, the proposed detection algorithm can adaptively optimize the divergence order and threshold according to observed data. In the simulation, the analytical FAR and MAR results are verified by the Monte Carlo simulations, and the proposed algorithm is shown to outperform the Kullback-Leibler divergence based 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".