Linganisha mbinu

	Uiguzi wa Monte Carlo Wenye Nguvu	Uchambuzi wa Hisia
Nyanja	Mbinu za Bayes	Ufanyaji Maamuzi
Familia	Bayesian methods	MCDM
Mwaka wa asili	1990s–2000s	2004
Mwanzilishi	Saltelli, Rubinstein, and the uncertainty-quantification community	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Aina	Robust simulation / uncertainty quantification	Robustness wrapper — parameter / weight perturbation sensitivity indices
Chanzo asilia	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	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester
Majina mbadala	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Zinazohusiana	6	0
Muhtasari	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.	SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.