Ãtude de la performance dâun algorithme Metropolis-Hastings avec ajustement\ndirectionnel
Bibliographic record
Abstract
Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont des outils très populaires\npour l’échantillonnage de lois de probabilité complexes et/ou en grandes dimensions.\nÉtant donné leur facilité d’application, ces méthodes sont largement répandues\ndans plusieurs communautés scientifiques et bien certainement en statistique, particulièrement\nen analyse bayésienne. Depuis l’apparition de la première méthode MCMC en\n1953, le nombre de ces algorithmes a considérablement augmenté et ce sujet continue\nd’être une aire de recherche active.\nUn nouvel algorithme MCMC avec ajustement directionnel a été récemment développé\npar Bédard et al. (IJSS, 9 :2008) et certaines de ses propriétés restent partiellement\nméconnues. L’objectif de ce mémoire est de tenter d’établir l’impact d’un paramètre clé\nde cette méthode sur la performance globale de l’approche. Un second objectif est de\ncomparer cet algorithme à d’autres méthodes MCMC plus versatiles afin de juger de sa\nperformance de façon relative.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".