Markov Chain Monte Carlo-Based Bayesian Analysis of Binary Response Regression, with Illustration in Dose-Response Assessment
Why this work is in the frame
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Bibliographic record
Abstract
This paper deals with the Bayesian analysis of binary response regression using Markov chain Monte Carlo (MCMC) methods, more specifically the Metropolis sampler, for posterior simulation. The methodology is illustrated with real-world data from a bioassay experiment. Inference about quantities of typical interest in the dose-response setting such as the lethal dose is discussed as well. MCMC are routinely implemented through popular Bayesian software such as Win-/Open-BUGS. However, these remain black boxes which provide no insight in the estimation procedure. This paper exemplifies that developing and implementing an MCMC sampler may, in many practical situations, be relatively straightforward. The R code for the Metropolis sampler is also provided in an appendix to the paper.
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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 | 0.027 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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