Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper describes Bayesian estimation of the parameters of the generalized extreme value (GEV) model with covariates. For this model the parameters of the GEV distribution are functions of covariates, allowing for dependent parameters and/or trends. A Markov chain Monte Carlo (MCMC) algorithm is generally used to estimate the posterior distributions of the parameters in a Bayesian framework. In this paper, the birth‐death MCMC (BDMCMC) procedure is developed in order to carry out both parameter estimation and Bayesian model selection. The BDMCMC methods allow the jump between models of different dimensions. The general algorithm consists of two types of sampling steps. The first one involves dimension‐changing moves, and the second is conditional on a fixed model. Parameters are estimated in a fully Bayesian framework, and the model is selected by the length of time that the MCMC chain remains in that model. Real and simulated data sets illustrate the usefulness of the proposed methodology.
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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 | 0.003 | 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 | 0.001 | 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 it