Data Clustering with Libby-Novick Beta-Liouville Mixture Models: A Minimum Message Length Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we examine the issue of figuring out a proportional data structure without prior knowledge of the number of clusters. We model the data samples by finite mixture models based on Libby-Novick Beta-Liouville distribution, a new distribution that combines the key features of both Libby-Novick Beta and Liouville distributions. It gives high flexibility and a more robust covariance structure compared to typical distributions such as Dirichlet. However, determining the number of clusters in mixture modeling is a significant issue. In this case, the number of clusters is ascertained by applying the minimum message length (MML) approach. Furthermore, the complexity of the mixture model (that is, the number of components) can be automatically and concurrently computed with the parameters estimated in a closed form as part of the Expectation Maximization process. The proposed method is validated by both synthetic data and real-world data.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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