Exact maximum likelihood time delay estimation
Why this work is in the frame
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Bibliographic record
Abstract
The authors present an exact solution to the problem of maximum-likelihood time-delay estimation over arbitrary observation time T. That is, the standard assumption T>> tau /sub c/+d/sub max/ made in the derivation of the asymptotic maximum-likelihood (AML) estimator, where t/sub c/ is the correlation time of the various processes involved and d/sub max/ the maximum permissible delay, is relaxed. The exact maximum-likelihood (EML) processor is shown to consist of a special finite-time beamformer, followed by a scalar postprocessor based on the eigenvalues and eigenfunctions of a certain integral equation. The solution of this integral equation is obtained for the case of stationary signals with rational power spectral densities (PSD). The performance of EML and AML are compared by means of computer simulations for a first-order low-pass PSD. The results show that EML can lead to a significant improvement in performances (bias, variance, large errors) when the condition T>> tau /sub c/+d/sub max/ is not satisfied.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 0.001 |
| 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