DAUD: A data driven algorithm to find discrete approximations of unknown continuous distributions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discrete approximation of continuous probability distributions is applied in solving large-scale intractable stochastic models in engineering, business and economics. While the existing approaches rely on the known continuous distribution; to our knowledge, no practical technique exists that approximates the unknown continuous processes. The need for such a technique is heightened with the rise of increasingly larger volumes of data generated by modern systems, while their underlying processes are not fully known. It is important to know that the quality of these approximations can be improved by refining the discretization, however, this comes at the cost of increased computational burden. We thus propose an algorithm that finds a good approximation with minimal discretization based on the convergence behavior of statistical moments. The algorithm was tested with data sets comprising 500 to 1,000,000 data points. The results show robust behavior of the algorithm, especially for the datasets with more than 10,000 data points and for various distribution shapes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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