Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Глобальный анализ чувствительности× | Метод Монте-Карло× | |
|---|---|---|
| Область≠ | Имитационное моделирование | Принятие решений |
| Семейство≠ | Process / pipeline | MCDM |
| Год появления≠ | 1973–2001 | 1949 |
| Автор метода≠ | I.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973) | Metropolis, N., Ulam, S. |
| Тип≠ | Variance-based sensitivity decomposition | Robustness wrapper — Monte Carlo uncertainty propagation |
| Основополагающий источник≠ | Sobol, I.M. (2001). Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates. Mathematics and Computers in Simulation, 55(1–3), 271–280. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Другие названия≠ | variance decomposition, Sobol indices, Morris screening, FAST method | — |
| Связанные≠ | 4 | 0 |
| Сводка≠ | Global sensitivity analysis (GSA) is a family of techniques that decompose the variance of a model's output across its input parameters, quantifying how much each input — and each combination of inputs — contributes to the total uncertainty in the result. Sobol's variance-based indices (2001), Morris's one-at-a-time (OAT) screening (1991), and the Fourier Amplitude Sensitivity Test (FAST, first proposed by Cukier et al. in 1973) are the three most widely used approaches. Together they serve as the standard toolkit for identifying which parameters drive model behaviour and which can be safely fixed. | 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. |
| ScholarGateНабор данных ↗ |
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