Incorporating Prior Information in Optimal Design for Model Selection
Bibliographic record
Abstract
An important use of experimental designs is in screening, in which experimenters seek to identify significant effects (both main effects and potentially interactions) from a large set of candidate effects. This article goes further than identification of effects, introducing a design criterion that seeks to maximize the ability to discriminate between models. Motivated by the work of Meyer, Steinberg, and Box, the Bayesian criterion is based on the Hellinger distance between predictive distributions under competing models. A bound for the criterion is obtained, greatly improving interpretability. The set of all possible models to compare is huge, and not all models are equally plausible. This challenge is addressed through prior distributions on the space of models that indicate preference for intuitively appealing models, such as those with few effects, more low-order than high-order effects, and inheritance structure between active main effects and interactions. Techniques for evaluating the criterion and searching for optimal designs are presented. The effectiveness of the criterion is illustrated with a number of examples that consider regular and nonregular designs, robust designs, and scenarios with partial prior knowledge of which effects are significant.
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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 | 0.050 | 0.156 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".