পদ্ধতির তুলনা করুন

	গুরুত্বপূর্ণ স্যাম্পলিং	স্তরবিন্যস্ত নমুনায়ন (Stratified Sampling)
ক্ষেত্র	অনুকরণ	জরিপ পদ্ধতি
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1951	1977
প্রবর্তক	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	William G. Cochran
ধরন	Monte Carlo variance-reduction technique	Probability-based survey sampling design
মৌলিক উৎস	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
অপর নাম	IS, weighted Monte Carlo, Önem Örneklemesi	Proportional Stratified Sampling, Optimal Allocation Sampling, Stratum-Based Sampling, Tabakalı Örnekleme
সম্পর্কিত	5	2
সারসংক্ষেপ	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.