Сравнение методов

	Байесовское моделирование очередей	Байесовское моделирование методом Монте-Карло
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1994	1987–1990s
Автор метода	Armero, C. & Bayarri, M. J.	O'Hagan, A. and colleagues
Тип	Bayesian inference + stochastic simulation	Simulation / uncertainty quantification
Основополагающий источник	Armero, C., & Bayarri, M. J. (1994). Bayesian prediction in M/M/1 queues. Queueing Systems, 15(1–4), 401–417.	O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992
Другие названия	BQS, Bayesian Queue Simulation, Bayesian Stochastic Queueing, Bayesian Queuing Analysis	Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation
Связанные	6	4
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.	Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms.