Sequential Markov Chain Monte Carlo (MCMC) model discrimination
Bibliographic record
Abstract
Abstract In this paper a new approach to model discrimination is presented that takes advantage of Markov Chain Monte Carlo (MCMC) methods. It combines an experimental criterion first proposed by Roth (Roth, Design of Experiments for Discrimination Among rival Models, PhD, Thesis, Princeton University, New Jersey, USA, 1965) with an adaptation of a model selection method described by Chib and Jeliazkov [Chib and Jeliazkov, Stat. Neerl. 59, 30–44 (2005)], which uses an Acceptance–Rejection Metropolis–Hastings algorithm to evaluate the model marginal likelihood thus enabling the calculation of model posterior probabilities. It does so without requiring any linearisation of nonlinear models. In designing model discrimination experiments using the Roth criterion, MCMC methods are again used to find the mean of the predicted values by integrating over the entire parameter probability density function. The method is illustrated using the well‐known chemical reaction kinetics example first discussed by Box and Hill [Box and Hill, Technometrics 9, 57–71 (1967)]. The results indicate that the method is very successful in identifying the correct model. Higher error levels and more complex kinetics require on average more model discrimination experiments. © 2012 Canadian Society for Chemical Engineering
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.
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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".