Objective Bayesian analysis of spatial models with separable correlation functions
Bibliographic record
Abstract
Abstract This paper considers general linear models for Gaussian geostatistical data with multi‐dimensional separable correlation functions involving multiple parameters. We derive various objective priors, such as the Jeffreys‐rule, independence Jeffreys, and usual and exact reference priors for the model parameters. In addition, we relax and simplify the assumptions in Paulo (2005) for the propriety of the posteriors in the general setup. We show that the frequentist coverage of posterior credible intervals for a function of range parameters do not depend on the regression coefficient or error variance. These objective priors and a proper flat prior based on ML estimates are compared by examining the frequentist coverage of equal‐tailed Bayesian credible intervals. An illustrative example is given from the field of complex computer model validations. The Canadian Journal of Statistics 41: 488–507; 2013 © 2013 Statistical Society of Canada
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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 | 0.000 | 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) | 0.003 | 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".