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Робусна Монте Карло симулација×Simulacija Monte Karlo×
OblastBajesovska statistikaDonošenje odluka
PorodicaBayesian methodsMCDM
Godina nastanka1990s–2000s1949
TvoracSaltelli, Rubinstein, and the uncertainty-quantification communityMetropolis, N., Ulam, S.
TipRobust simulation / uncertainty quantificationRobustness wrapper — Monte Carlo uncertainty propagation
Temeljni izvorSaltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Drugi nazivirobust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo
Srodne60
SažetakRobust Monte Carlo simulation extends standard Monte Carlo by explicitly accounting for uncertainty in input distributions, model structure, or parameter assumptions. Rather than assuming a single fixed probability distribution for each input, the analyst considers a family of plausible distributions and evaluates how sensitive the output is to those choices, yielding conclusions that hold across a range of reasonable assumptions.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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ScholarGateUporedite metode: Robust Monte Carlo Simulation · MONTE-CARLO-SIMULATION. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare