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| Stochastische ereignisdiskrete Simulation× | Markov-Modell× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1960s–1970s | 1906 |
| Urheber≠ | Banks, Carson, Nelson, Nicol; Law, A. M. | Andrei Markov |
| Typ≠ | Stochastic simulation model | Probabilistic state-transition model |
| Wegweisende Quelle≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Aliasnamen | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. | 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. |
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