方法对比
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| 贝叶斯离散事件仿真× | 随机离散事件仿真× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s–2010s | 1960s–1970s |
| 提出者≠ | Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s | Banks, Carson, Nelson, Nicol; Law, A. M. |
| 类型≠ | Hybrid simulation-inference framework | Stochastic simulation model |
| 开创性文献≠ | Onggo, B. S., & Kunc, M. (2016). Combining discrete-event simulation and Bayesian updating for incorporating evidence from real-world data. Journal of Simulation, 10(1), 1-12. link ↗ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| 别名 | Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES |
| 相关 | 6 | 6 |
| 摘要≠ | Bayesian Discrete-Event Simulation (BDES) integrates Bayesian statistical inference with discrete-event simulation. Prior beliefs about system parameters — such as service rates, arrival times, or failure probabilities — are updated with observed data via Bayes' theorem, and the resulting posterior distributions directly drive the simulation engine. This coupling allows modelers to propagate both aleatory and epistemic uncertainty through event-driven process models. | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. |
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