An Aspect Oriented Framework to Applying Markov Chain Monte Carlo Methods with Dynamic Models
Bibliographic record
Abstract
Both dynamic modeling and Bayesian Markov Chain Monte Carlo (MCMC) methods are established as increasingly popular approaches in their own domains. Dynamic modeling, although widely used to address complex situations, often suffers shortage of empirical data for model parameterization. Dynamic modelers thus use calibration to estimate parameters for which direct evidence is lacking. Unfortunately calibration suffers limitations in capturing the global (for multi-modal distribution) structure of parameter distributions, and a lack of a means of translating uncertainty in parameter estimates directly into uncertainty with respect to model outcomes. We present here a generic user-friendly aspect-based implementation of a theoretically grounded approach to address these limitations by combining Bayesian MCMC methods with dynamic models to estimate model parameters by sampling from joint posterior parameter distributions. The framework is enriched by a user interface to enable the parameter selection at run-time and an interactive run-time graphical visualization of parameter traceplots is generated during MCMC operation. To enable this, a probabilistic model -- including a prior distribution and a likelihood function -- needs to be specified within the dynamic model. The framework, when enabled, performs MCMC experiments using the dynamic and probabilistic models. We describe here the framework, experiments conducted, and the results obtained.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".