Process / pipelineSimulation / optimization
贝叶斯离散事件仿真 — 后验信息驱动的随机过程建模
贝叶斯离散事件仿真（Bayesian Discrete-Event Simulation, BDES）将贝叶斯统计推断与离散事件仿真相结合。关于系统参数（如服务率、到达时间或失效率）的先验信念通过贝叶斯定理与观测数据相结合而更新，由此产生的后验分布直接驱动仿真引擎。这种耦合允许建模者在事件驱动的过程模型中同时传播偶然不确定性和认知不确定性。
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来源
- 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 ↗
- Pidd, M. (2004). Computer Simulation in Management Science (5th ed.). Wiley. ISBN: 9780470092781
如何引用本页
ScholarGate. (2026, June 3). Bayesian Discrete-Event Simulation — Posterior-informed stochastic process modeling. ScholarGate. https://scholargate.app/zh/simulation/bayesian-discrete-event-simulation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Agent-Based Discrete-Event Simulation仿真↔ compare
- 贝叶斯基于智能体的建模仿真↔ compare
- 贝叶斯马尔可夫模型仿真↔ compare
- 离散事件仿真 (DES)仿真↔ compare
- 蒙特卡洛模拟决策↔ compare
- 随机离散事件仿真仿真↔ compare