方法对比

	贝叶斯离散事件仿真	离散事件仿真 (DES)
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	2000s–2010s	1960s (formalized); modern computational form from 1970s onward
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 (textbook lineage); foundational work by Tocher & Conway (1960s)
类型	Hybrid simulation-inference framework	Stochastic process simulation
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.	Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127
别名	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	DES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
相关	6	4
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.	Discrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.