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Globalna analiza osjetljivosti×Simulacija Monte Carlo×
PodručjeSimulacijaDonošenje odluka
ObiteljProcess / pipelineMCDM
Godina nastanka1973–20011949
TvoracI.M. Sobol (indices, 2001); Morris (screening, 1991); Cukier et al. (FAST, 1973)Metropolis, N., Ulam, S.
VrstaVariance-based sensitivity decompositionRobustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvorSobol, 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 ↗
Drugi nazivivariance decomposition, Sobol indices, Morris screening, FAST method
Srodne40
SažetakGlobal 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.
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ScholarGateUsporedite metode: Global Sensitivity Analysis · MONTE-CARLO-SIMULATION. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare