방법 비교

	베이즈 대기행렬 시뮬레이션	몬테카를로 시뮬레이션
분야	시뮬레이션	의사결정
계열	Process / pipeline	MCDM
기원 연도	1994	1949
창시자	Armero, C. & Bayarri, M. J.	Metropolis, N., Ulam, S.
유형	Bayesian inference + stochastic simulation	Robustness wrapper — Monte Carlo uncertainty propagation
원전	Armero, C., & Bayarri, M. J. (1994). Bayesian prediction in M/M/1 queues. Queueing Systems, 15(1–4), 401–417.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
별칭	BQS, Bayesian Queue Simulation, Bayesian Stochastic Queueing, Bayesian Queuing Analysis	—
관련	6	0
요약	Bayesian Queueing Simulation combines Bayesian statistical inference with stochastic queueing simulation to model waiting-line systems under parameter uncertainty. Instead of treating arrival and service rates as fixed known values, it places prior distributions over them, updates these with observed data to obtain posteriors, and propagates the resulting parameter uncertainty through repeated simulation runs to produce probabilistic predictions of system performance metrics such as queue length, waiting time, and server utilisation.	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.