Bayes factor biases for non‐nested models and corrections
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
Abstract With the advent of simulation‐based methods to obtain samples from posteriors and due to increases in computational power, Bayesian methods are increasingly applied to complex problems, sometimes providing the only available methods where likelihood implementations are difficult. As a consequence a large body of research in science and social science increasingly utilizes Bayesian tools, often applying them with default settings. A fundamental problem of interest is model selection, and Bayes factors provide a natural approach to Bayesian model selection. Using Laplace approximations and illustrative examples we demonstrate that Bayes factors can have strong biases toward particular models even in non‐nested settings with the same number of parameters. Several easily implemented corrections are shown to provide effective cross‐checks to default Bayes Factors. The Canadian Journal of Statistics 45: 290–309; 2017 © 2017 Statistical Society of Canada
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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 | 0.000 | 0.010 |
| 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 it