Bandingkan metode

	Simulasi Monte Carlo Robust	Analisis Sensitivitas
Bidang	Bayesian	Pengambilan Keputusan
Keluarga	Bayesian methods	MCDM
Tahun asal	1990s–2000s	2004
Pencetus	Saltelli, Rubinstein, and the uncertainty-quantification community	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Tipe	Robust simulation / uncertainty quantification	Robustness wrapper — parameter / weight perturbation sensitivity indices
Sumber perintis	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
Alias	robust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo	—
Terkait	6	0
Ringkasan	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.