Comparar métodos

	Simulación de Monte Carlo Robusta	Simulación Bootstrap
Campo	Bayesiano	Simulación
Familia	Bayesian methods	Process / pipeline
Año de origen	1990s–2000s	1979
Autor original	Saltelli, Rubinstein, and the uncertainty-quantification community	Bradley Efron
Tipo	Robust simulation / uncertainty quantification	Simulation-based nonparametric inference
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	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.
Alias	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)
Relacionados	6	5
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.	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.