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	Robust Monte Carlo Simulation	Symulacja Monte Carlo
Dziedzina	Statystyka bayesowska	Podejmowanie decyzji
Rodzina	Bayesian methods	MCDM
Rok powstania	1990s–2000s	1949
Twórca	Saltelli, Rubinstein, and the uncertainty-quantification community	Metropolis, N., Ulam, S.
Typ	Robust simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Źródło pierwotne	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Inne nazwy	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Pokrewne	6	0
Podsumowanie	Robust 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.