Improving separating function estimation tests using Bayesian approaches
Why this work is in the frame
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Bibliographic record
Abstract
Separating function estimation tests (SFETs) replace detection problems with an estimation problem. In this paper, we study the relationship between improving the estimation of unknown parameters using Bayesian approaches and the performance of the corresponding SFET. Although the estimation method in the SFET is deterministic, we show that applying Bayesian methods to estimate the rest of unknown parameters that are not involved in the SF provide improved SFET performance. We illustrate this idea using two important problems. In the first example, we consider a sinusoid signal with unknown parameters in white noise. We show that a softmax function using the Fourier transform of the signal is a proper probability density function (pdf) for the frequency to improve the performance of the SFET. In the second example, a more accurate estimation of the unknown parameters of the signal is achieved, using the Minimum Mean Square Error (MMSE) estimation of the random signal corrupted by white noise.
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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 | 0.001 |
| 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