Predictive densities from the Rayleigh Life Model under Type II censored samples
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A tremendous amount of life data has been collected and analyzed in connection with recent advances in engineering and the biomedical sciences. It is desirable to make use of statistical and computational techniques that are at the cutting edge in order to reach valid conclusions about the nature of the underlying model. The Rayleigh distribution has been widely used for modelling life data and most studies on this distribution concentrate on inference about the parameters or on the reliability and hazard functions. This paper is concerned with predictive inference for future responses from a Rayleigh distribution given a type II censored sample by using the Bayesian approach. We are considering both one-parameter and two-parameter Rayleigh distributions. Predictive densities for future responses are derived with hyperparameters to obtain informative results about future responses. Keywords and phrases: type II censored sampleRayleigh life modelBayesian approachposterior densitypredictive density
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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