Supervised Bayesian Learning Using MCMC Methods. Application to the . . .
Bibliographic record
Abstract
Introduction to Stochastic processes, Prentice-Hall, 1975. [13] R. Bellazi P. Magni and G. De Nicolao, "Bayesian function learning using MCMC methods", IEEE Trans- actions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 1319-1331, 1998. [14] R. E. Kass and A. E. Raftery, "Bayes factors", Journal of the American Statistical Association, vol. 90, pp. 773-795, 1995. [15] M. Lavine and M. West, "A Bayesian method for classification and discrimination", The Canadian Journal of Stats., vol. 20, no. 4, pp. 451-461, 1992. [16] M. Davy, C. Doncarli, and G. Faye Boudreaux-Bartels, "Improved optimization of time-frequency based signal classifiers", IEEE Signal processing Letters, vol. 8, no. 2, pp. 52 - 57, February 2001. [17] M. Davy, Noyaux optimiss pour la classification dans le plan temps-frquence, Ph.D. thesis, Universit de Nantes, 2000, in French. [18] M. Davy and C. Doncarli, "Optimal kernels of time-frequency representations for signal classification", in IEEE Internation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".