Módszerek összehasonlítása

	Bayesian Discrete-Event Simulation	MONTE-CARLO-SIMULATION
Tudományterület	Szimuláció	Döntéshozatal
Módszercsalád	Process / pipeline	MCDM
Keletkezés éve	2000s–2010s	1949
Megalkotó	Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s	Metropolis, N., Ulam, S.
Típus	Hybrid simulation-inference framework	Robustness wrapper — Monte Carlo uncertainty propagation
Alapmű	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
Alternatív nevek	Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation	—
Kapcsolódó	6	0
Összefoglaló	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.