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 lâéchantillonnage de lois de probabilité complexes et/ou en grandes dimensions.\nÃtant donné leur facilité dâapplication, ces méthodes sont largement répandues\ndans plusieurs communautés scientifiques et bien certainement en statistique, particulièrement\nen analyse bayésienne. Depuis lâapparition de la première méthode MCMC en\n1953, le nombre de ces algorithmes a considérablement augmenté et ce sujet continue\ndâêtre 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. Lâobjectif de ce mémoire est de tenter dâétablir lâimpact dâun paramètre clé\nde cette méthode sur la performance globale de lâapproche. Un second objectif est de\ncomparer cet algorithme à dâautres méthodes MCMC plus versatiles afin de juger de sa\nperformance de façon relative.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".