Comparer des méthodes

	Échantillonnage par importance	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	1951	1949
Auteur d'origine	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	Metropolis, N., Ulam, S.
Type	Monte Carlo variance-reduction technique	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	IS, weighted Monte Carlo, Önem Örneklemesi	—
Apparentées	5	0
Résumé	Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs.	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.