Methoden vergelijken

	Robuuste Monte Carlo-simulatie	Monte Carlo Simulatie
Vakgebied	Bayesiaanse statistiek	Besluitvorming
Familie	Bayesian methods	MCDM
Jaar van ontstaan	1990s–2000s	1949
Grondlegger	Saltelli, Rubinstein, and the uncertainty-quantification community	Metropolis, N., Ulam, S.
Type	Robust simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Oorspronkelijke bron	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
Aliassen	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Verwant	6	0
Samenvatting	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.