Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Symulacja Kolejkowa w Ujęciu Bayesowskim× | Model Markowa× | |
|---|---|---|
| Dziedzina | Symulacja | Symulacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1994 | 1906 |
| Twórca≠ | Armero, C. & Bayarri, M. J. | Andrei Markov |
| Typ≠ | Bayesian inference + stochastic simulation | Probabilistic state-transition model |
| Źródło pierwotne≠ | Armero, C., & Bayarri, M. J. (1994). Bayesian prediction in M/M/1 queues. Queueing Systems, 15(1–4), 401–417. DOI ↗ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Inne nazwy | BQS, Bayesian Queue Simulation, Bayesian Stochastic Queueing, Bayesian Queuing Analysis | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
| ScholarGateZbiór danych ↗ |
|
|