Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stohastiskā scenāriju analīze× | Monte Carlo simulācija× | |
|---|---|---|
| Nozare≠ | Simulācija | Lēmumu pieņemšana |
| Saime≠ | Process / pipeline | MCDM |
| Izcelsmes gads≠ | 1955–1980s | 1949 |
| Autors≠ | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition | Metropolis, N., Ulam, S. |
| Tips≠ | Probabilistic scenario enumeration and evaluation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Pirmavots≠ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Citi nosaukumi≠ | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis | — |
| Saistītās≠ | 4 | 0 |
| Kopsavilkums≠ | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. | 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. |
| ScholarGateDatu kopa ↗ |
|
|