So sánh phương pháp

	Mô phỏng Monte Carlo Mạnh mẽ	Phân tích độ nhạy
Lĩnh vực	Bayes	Ra quyết định
Họ	Bayesian methods	MCDM
Năm ra đời	1990s–2000s	2004
Người khởi xướng	Saltelli, Rubinstein, and the uncertainty-quantification community	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Loại	Robust simulation / uncertainty quantification	Robustness wrapper — parameter / weight perturbation sensitivity indices
Công trình gốc	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
Tên gọi khác	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Liên quan	6	0
Tóm tắt	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.