Сравнение методов

	Байесовское моделирование очередей	Метод Монте-Карло
Область	Имитационное моделирование	Принятие решений
Семейство	Process / pipeline	MCDM
Год появления	1994	1949
Автор метода	Armero, C. & Bayarri, M. J.	Metropolis, N., Ulam, S.
Тип	Bayesian inference + stochastic simulation	Robustness wrapper — Monte Carlo uncertainty propagation
Основополагающий источник	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
Другие названия	BQS, Bayesian Queue Simulation, Bayesian Stochastic Queueing, Bayesian Queuing Analysis	—
Связанные	6	0
Сводка	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.