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| Bayesian Sensitivity Analysis× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 1984–1994 | 1949 |
| Urheber≠ | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) | Metropolis, N., Ulam, S. |
| Typ≠ | Uncertainty propagation and sensitivity quantification | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis | — |
| Verwandt≠ | 5 | 0 |
| Zusammenfassung≠ | Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty. | 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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