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| Multilevel-Monte-Carlo-Simulation× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Bayes-Statistik | Entscheidungsfindung |
| Familie≠ | Bayesian methods | MCDM |
| Entstehungsjahr≠ | 2008 | 1949 |
| Urheber≠ | Michael B. Giles | Metropolis, N., Ulam, S. |
| Typ≠ | variance-reduction simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Giles, M. B. (2008). Multilevel Monte Carlo path simulation. Operations Research, 56(3), 607–617. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | MLMC, multilevel MC, multi-level Monte Carlo, MLMC simulation | — |
| Verwandt≠ | 4 | 0 |
| Zusammenfassung≠ | Multilevel Monte Carlo (MLMC) is a variance-reduction technique that estimates expectations by combining simulations run at multiple levels of numerical resolution. Coarse, cheap simulations capture most of the signal; fine, expensive simulations correct only the remaining small difference — dramatically reducing total computational cost compared with standard Monte Carlo at the finest level alone. | 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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