On the empirical efficiency of local MCMC algorithms with pools of proposals
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In an attempt to improve on the Metropolis algorithm, various MCMC methods involving pools of proposals, such as the multiple‐try Metropolis and delayed rejection strategies, have been proposed. These methods generate several candidates in a single iteration; accordingly they are computationally more intensive than the Metropolis algorithm. In this paper, we consider three samplers with pools of proposals—the multiple‐try Metropolis algorithm, the multiple‐try Metropolis hit‐and‐run algorithm, and the delayed rejection Metropolis algorithm with antithetic proposals—and investigate the net performance of these methods in various contexts. To allow for a fair comparison, the study is carried under optimal mixing conditions for each of these samplers. The algorithms are used in the contexts of Bayesian logistic regressions, inference for a linear regression model, high‐dimensional hierarchical model, and bimodal distribution. The Canadian Journal of Statistics 41: 657–678; 2013 © 2013 Statistical Society of Canada
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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 | 0.001 | 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