Bayesian MAP estimation using Gaussian and diffused‐gamma prior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract For sparse and high‐dimensional data analysis, a valid approximation of ‐norm has played a key role. However, there is not much study on the ‐norm approximation in the Bayesian literature. In this article, we introduce a new prior, called Gaussian and diffused‐gamma prior, which leads to a nice ‐norm approximation under the maximum a posteriori estimation. To develop a general likelihood function, we utilize a general class of divergence measures, called Bregman divergence. Due to the generality of Bregman divergence, our method can handle various types of data such as count, binary, continuous, etc. In addition, our Bayesian approach provides many theoretical and computational advantages. To demonstrate the validity and reliability, we conduct simulation studies and real data analysis. The Canadian Journal of Statistics 46: 399–415; 2018 © 2018 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 | 0.003 |
| 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 it