An Adaptive Sampling Strategy to Accelerate Markov Chains Monte Carlo
Bibliographic record
Abstract
Summary Markov chain Monte Carlo (McMC) is a technique to sample the posterior distributions in order to quantify the uncertainty and update our knowledge about the model parameters. However, this technique is computationally costly. Therefore, it is frequently combined with a cheap-to-evaluate proxy model. Building an efficient and accurate proxy model using one-shot sampling method (such as Latin-Hypercube) is a challenging task as we do not know a prior the best sample locations and the number of required samples to tune our proxy model. In this paper, we present a novel adaptive sampling techniques using LOLA-Voronoi and Expected Improvement techniques to sequentially update our proxy (here ordinary Kriging model) using best sampling locations and density. This algorithm is used to balance between the exploration and exploitation to create an accurate proxy model for the IC fault model’s misfit function. The results show a large improvement comparing to the one-shot Latin-Hypercube design. The built proxy is then employed to reduce the computational cost of McMC process to find the posterior distributions of the model parameters.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".