A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation
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
For several areas in the domain of operations research and management science, such as service, logistics and supply chain, and financial systems, the randomness of arrivals is one primary source of uncertainty. Appropriately modeling, statistically characterizing, and efficiently simulating the arrival processes are critical for policy and performance evaluation in the related systems. Classic Monte Carlo simulators have advantages in capturing the interpretable “physics” of a stochastic object, whereas neural network–based simulators have advantages in capturing less-interpretable complicated dependence within a high-dimensional distribution. In “A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation,” Zheng, Zheng, and Zhu propose a doubly stochastic simulator that integrates a stochastic generative neural network and a classic Monte Carlo Poisson simulator to utilize the advantages of both. They provide statistical guarantees and demonstrate empirical performances of the proposed methods.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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