পদ্ধতির তুলনা করুন

	গুরুত্বপূর্ণ স্যাম্পলিং	মন্টে কার্লো সিমুলেশন
ক্ষেত্র	অনুকরণ	সিদ্ধান্ত গ্রহণ
পরিবার	Process / pipeline	MCDM
উদ্ভবের বছর	1951	1949
প্রবর্তক	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	Metropolis, N., Ulam, S.
ধরন	Monte Carlo variance-reduction technique	Robustness wrapper — Monte Carlo uncertainty propagation
মৌলিক উৎস	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
অপর নাম	IS, weighted Monte Carlo, Önem Örneklemesi	—
সম্পর্কিত	5	0
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.	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.