Bibliographic record
Abstract
A useful step in data analysis is clustering, in which observations are grouped together in a hopefully meaningful way. The mainstay model for Bayesian nonparametric clustering is the Dirichlet process mixture model, which has one key advantage of inferring the number of clusters automatically. However, the Dirichlet process mixture model has particular characteristics, such as linear growth in the size of clusters and exchangeability, that may not be suitable modelling choices for some data sets, so there is further research to be done into other Bayesian nonparametric models with characteristics that differ from that of the Dirichlet process mixture model while maintaining automatic inference of the number of clusters. In this thesis, we introduce the Neutral-to-the-Left mixture model, a family of Bayesian nonparametric infinite mixture models which serves as a strict generalization of the Dirichlet process mixture model. This family of mixture models has two key parameters: the distribution of arrival times of new clusters, and the parameters of the stick breaking distribution, whose customization allows the user to inject prior beliefs regarding the structure of the clusters into the model. We describe collapsed Gibbs and Metropolis–Hastings samplers to infer the posterior distribution of clusterings given data. We consider one particular parameterization of the Neutral-to-the-Left mixture model with characteristics that are distinct from that of the Dirichlet process mixture model, evaluate its performance on simulated data, and compare these to results from a Dirichlet process mixture model. Finally, we explore the utility of the Neutral-to-the-Left mixture model on real data by applying the model to cluster tweets.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".