Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesian Scenario Analysis× | Simulace Monte Carlo× | |
|---|---|---|
| Obor≠ | Simulace | Rozhodování |
| Rodina≠ | Process / pipeline | MCDM |
| Rok vzniku≠ | 2000s | 1949 |
| Tvůrce≠ | Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s) | Metropolis, N., Ulam, S. |
| Typ≠ | Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis | Robustness wrapper — Monte Carlo uncertainty propagation |
| Původní zdroj≠ | Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Další názvy≠ | BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis | — |
| Příbuzné≠ | 5 | 0 |
| Shrnutí≠ | Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures. | 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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