A Nonparametric Bayesian Framework for Multivariate Beta Mixture Models
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Bibliographic record
Abstract
In this paper, we propose a nonparametric Bayesian learning framework for clustering problem based on multivariate Beta mixture model. Mixture models have been widely used as an unsupervised learning method in many machine learning, data mining and pattern recognition applications. One critical challenge in applying mixture model is the selection of the proper number of mixture components which best describes the data. Our approach can be viewed as an extension of the finite mixture model to infinite to tackle the model selection problem. In particular, our learning approach is Bayesian and relies on estimation of posterior distribution using Markov Chain Monte Carlo technique. The performance of our proposed method is evaluated through multiple challenging applications and we show that clustering via infinite multivariate Beta mixture models provides a more powerful performance comparing with various other approaches.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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