Modeling and Clustering Positive Vectors via Nonparametric Mixture Models of Liouville Distributions
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Bibliographic record
Abstract
In this article, we propose an effective mixture model-based approach to modeling and clustering positive data vectors. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution which is extracted from the family of Liouville distributions. To cope with the problem of determining the appropriate number of clusters in our approach, a nonparametric Bayesian framework is used to extend the IBL mixture to an infinite mixture model in which the number of clusters is assumed to be infinite initially and will be inferred automatically during the learning process. To optimize the proposed model, we propose a convergence-guaranteed learning algorithm based on the averaged collapsed variational Bayes inference that can effectively learn model parameters with closed-form solutions. The effectiveness of the proposed infinite IBL mixture model for modeling and clustering positive vectors is validated through both synthetic and real-world data sets.
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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 | 0.001 |
| 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