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| Bayesian Discrete-Event Simulation× | Montecarlosimulering× | |
|---|---|---|
| Ämnesområde≠ | Simulering | Beslutsfattande |
| Familj≠ | Process / pipeline | MCDM |
| Ursprungsår≠ | 2000s–2010s | 1949 |
| Upphovsperson≠ | Developed across operations research and Bayesian statistics communities; prominently formalized in health economic simulation in the 2000s–2010s | Metropolis, N., Ulam, S. |
| Typ≠ | Hybrid simulation-inference framework | Robustness wrapper — Monte Carlo uncertainty propagation |
| Ursprungskälla≠ | Onggo, B. S., & Kunc, M. (2016). Combining discrete-event simulation and Bayesian updating for incorporating evidence from real-world data. Journal of Simulation, 10(1), 1-12. link ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | Bayesian DES, BDES, Bayesian event-driven simulation, posterior-driven discrete-event simulation | — |
| Närliggande≠ | 6 | 0 |
| Sammanfattning≠ | Bayesian Discrete-Event Simulation (BDES) integrates Bayesian statistical inference with discrete-event simulation. Prior beliefs about system parameters — such as service rates, arrival times, or failure probabilities — are updated with observed data via Bayes' theorem, and the resulting posterior distributions directly drive the simulation engine. This coupling allows modelers to propagate both aleatory and epistemic uncertainty through event-driven process models. | 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. |
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