Comparer des méthodes

	Échantillonnage par importance	Échantillonnage stratifié
Domaine	Simulation	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	1951	1977
Auteur d'origine	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	William G. Cochran
Type	Monte Carlo variance-reduction technique	Probability-based survey sampling design
Source fondatrice	Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley. ISBN: 978-0-471-16240-7
Alias	IS, weighted Monte Carlo, Önem Örneklemesi	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
Apparentées	5	2
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.	Stratified sampling is a probability sampling design in which the target population is partitioned into non-overlapping, exhaustive subgroups called strata, and independent probability samples are drawn within each stratum. Formalized by William G. Cochran in Sampling Techniques (1977), the method exploits known population structure to reduce variance and guarantee representativeness of all major subgroups, making it a cornerstone of large-scale survey research and official statistics.