Bayesian Inference with Markov Chain Monte Carlo–Based Numerical Approach for Input Model Updating
Bibliographic record
Abstract
Stochastic, discrete-event simulation modeling has emerged as a useful tool for facilitating decision making in construction. Owing to the rigidity inherent to distribution-based inputs, current simulation models have difficulty incorporating new data in real-time, and fusing these data with subjective judgments. Accordingly, application of this valuable technique is often limited to project planning stages. To expand implementation of simulation-based decision-support systems to the execution phase, this research proposes the use of Bayesian inference with Markov chain Monte Carlo (MCMC)–based numerical approximation approach as a universal input model updating methodology of stochastic simulation models for any given univariate continuous probability distribution. Found capable of (1) fusing actual performance with expert judgment, (2) integrating actual performance with historical data, and (3) processing raw data by absorbing uncertainties and randomness, the proposed method will considerably improve the resilience, reliability, accuracy, and practicality of stochastic simulation models, thereby enabling the application of stochastic simulation in the execution phase of construction.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".