Сравнение методов

	Байесовское дискретно-событийное моделирование	Метод Монте-Карло
Область	Имитационное моделирование	Принятие решений
Семейство	Process / pipeline	MCDM
Год появления	2000s–2010s	1949
Автор метода	Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s	Metropolis, N., Ulam, S.
Тип	Hybrid simulation-inference framework	Robustness wrapper — Monte Carlo uncertainty propagation
Основополагающий источник	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.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Другие названия	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	—
Связанные	6	0
Сводка	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.