Learning of Finite Two-Dimensional Beta Mixture Models
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
Finite mixture models are widely applied in various domains of applications. They assist to analyze datasets, achieve better insight into the nature of data, discover latent patterns and provide critical knowledge that we are looking for. The main focus in past works was on Gaussian statistical models, however there are several applications involving asymmetric and non-Gaussian data. Parameter estimation is one of the essential and fundamental challenges of statistical researches. Some deterministic approaches such as expectation maximization (EM) have been mainly considered as effective techniques to deal with this issue. In this article, we introduce a bivariate Beta distribution with three parameters as our main parent distribution which could be applied in skin detection and image segmentation. The feasibility and effectiveness of the proposed method are demonstrated by experimental results that concern both artificial and real datasets.
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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 | 0.000 | 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