Finite Two-Dimensional Beta Mixture Models: Model Selection and Applications
Why this work is in the frame
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Bibliographic record
Abstract
Finite mixture models are powerful and progressively important probabilistic tools in machine learning. The practicality of these inference engines is widely acknowledged by employing them in various areas of science and technology which involve the statistical modeling of multimodal and complex data. One of the crucial tasks that should be addressed in mixture models and unsupervised learning problem is defining the number of clusters which best describe the data. This article proposes a clustering framework for learning a finite mixture model based on a bivariate Beta distribution with three parameters and the proper number of clusters is determined by Minimum Message Length (MML). The feasibility and effectiveness of our work are demonstrated by real world challenging applications such as image segmentation and occupancy estimation in smart buildings.
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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 | 0.001 |
| 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