Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo
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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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".