Comparar métodos

	Simulación de Monte Carlo Robusta	Simulación de Monte Carlo
Campo	Bayesiano	Toma de decisiones
Familia	Bayesian methods	MCDM
Año de origen	1990s–2000s	1949
Autor original	Saltelli, Rubinstein, and the uncertainty-quantification community	Metropolis, N., Ulam, S.
Tipo	Robust simulation / uncertainty quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Fuente seminal	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
Alias	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Relacionados	6	0
Resumen	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.