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Robust Monte Carlo-simulering×Følsomhedsanalyse×
FagområdeBayesianskBeslutningstagning
FamilieBayesian methodsMCDM
Oprindelsesår1990s–2000s2004
OphavspersonSaltelli, Rubinstein, and the uncertainty-quantification communitySaltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
TypeRobust simulation / uncertainty quantificationRobustness wrapper — parameter / weight perturbation sensitivity indices
Oprindelig kildeSaltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗
Aliasserrobust MC simulation, Monte Carlo robustness analysis, robust stochastic simulation, uncertainty-robust Monte Carlo
Relaterede60
ResuméRobust 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.
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ScholarGateSammenlign metoder: Robust Monte Carlo Simulation · SENSITIVITY-ANALYSIS. Hentet 2026-06-15 fra https://scholargate.app/da/compare