Comparer des méthodes

	Techniques de réduction de variance pour la simulation de Monte-Carlo	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1950s–1980s (technique family)	1949
Auteur d'origine	Hammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953)	Metropolis, N., Ulam, S.
Type	Simulation variance-reduction technique family	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	Ross, S.M. (2012). Simulation (5th ed.). Academic Press. ISBN: 978-0124158252	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	antithetic variates, control variates, importance sampling, stratified sampling MC	—
Apparentées	4	0
Résumé	Variance reduction techniques are a family of methods that improve the efficiency of Monte Carlo simulation by achieving the same estimation accuracy with fewer random draws. Developed incrementally from the 1950s onward — with antithetic variates attributed to Hammersley and Morton, control variates formalised by Lavenberg and Welch, and importance sampling rooted in Kahn and Marshall — the family includes antithetic variates (AV), control variates (CV), importance sampling (IS), and stratification, each exploiting a different structural property of the target quantity to lower estimator variance without introducing bias.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.