Random discrete probability measures based on a negative binomial process
Bibliographic record
Abstract
Abstract A distinctive functional of the Poisson point process is the negative binomial process for which the increments are not independent but are independent conditional on an underlying gamma variable. Using a new point process representation for the negative binomial process, we generalize the Poisson–Kingman distribution and its corresponding random discrete probability measure. This new proposed family of discrete random probability measures, which is defined by normalizing the points of the negative binomial process, provides a new set of useful priors for Bayesian nonparametric models with more flexibility than the random discrete probability measure which are obtained by normalizing the points of a Poisson point process. We illustrate how this family of random discrete probability measures contains the nonparametric Bayesian priors such as the Dirichlet process, the normalized positive ‐stable process, the Poisson–Dirichlet process (PDP), and others. With the same gamma Lévy measure, we derive an extension of the Dirichlet process and its almost sure approximation. Using our representation for the negative binomial process, we develop a new series representation for the PDP. We demonstrate through simulations how using priors from this family can enhance the accuracy of Bayesian nonparametric hierarchical models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".