Сравнение на методи

	Бейсианска симулация на дискретни събития	Стохастична симулация по метода на дискретните събития
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2000s–2010s	1960s–1970s
Създател	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.
Тип	Hybrid simulation-inference framework	Stochastic simulation model
Основополагащ източник	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.). Prentice Hall. ISBN: 9780136062127
Други названия	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES
Свързани	6	6
Резюме	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.