Bayesian sequential I-optimal designs for split-plot experiments under model uncertainty
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
Split-plot designs have enjoyed great popularity since their inception. The I-optimality criterion is frequently employed to select split-plot designs that exhibit good predictive performance under a specified model. However, in situations where the true model is highly uncertain and/or the assumed model is misspecified, I-optimal split-plot designs may lack efficiency in fitting the true model. To address this issue, we propose the Bayesian version of the I-optimality criterion for split-plot experiments, encompassing both primary and potential terms in the full model. Subsequently, we extend the Bayesian I-optimal split-plot design into a two-stage sequential framework, in which the first-stage design is constructed based on this Bayesian criterion, and experimental data are analyzed to rearrange potential terms according to their activeness, then the second-stage design is selected via an augmented I-optimality criterion under the rearranged primary model. Through comparison with counterparts using several numerical examples and a practical experiment, the proposed Bayesian I-I optimal split-plot designs demonstrate superior performance. In addition, further numerical results are discussed in the Supplementary Materials.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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