So sánh phương pháp

	Lấy mẫu trọng số	Mô phỏng Monte Carlo
Lĩnh vực	Mô phỏng	Ra quyết định
Họ	Process / pipeline	MCDM
Năm ra đời	1951	1949
Người khởi xướng	Herman Kahn & Theodore Harris (RAND Corporation, 1951)	Metropolis, N., Ulam, S.
Loại	Monte Carlo variance-reduction technique	Robustness wrapper — Monte Carlo uncertainty propagation
Công trình gốc	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
Tên gọi khác	IS, weighted Monte Carlo, Önem Örneklemesi	—
Liên quan	5	0
Tóm tắt	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.