Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Monte Carlo simulācija× | Stochastic Sensitivity Analysis× | |
|---|---|---|
| Nozare≠ | Lēmumu pieņemšana | Simulācija |
| Saime≠ | MCDM | Process / pipeline |
| Izcelsmes gads≠ | 1949 | 1990s–2000s |
| Autors≠ | Metropolis, N., Ulam, S. | Saltelli, A. et al.; Claxton, K. et al. (health economics stream) |
| Tips≠ | Robustness wrapper — Monte Carlo uncertainty propagation | Probabilistic uncertainty quantification technique |
| Pirmavots≠ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 |
| Citi nosaukumi≠ | — | PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis |
| Saistītās≠ | 0 | 5 |
| Kopsavilkums≠ | 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. | Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty. |
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