Incorporating Prior Information in Optimal Design for Model Selection
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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