方法对比

	自适应加权抽样	Importance Sampling
领域	调查方法论	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1990s–2000s	1951
提出者	Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature	Herman Kahn & Theodore Harris (RAND Corporation, 1951)
类型	Probabilistic sampling procedure	Monte Carlo variance-reduction technique
开创性文献	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.
别名	AWS, adaptive importance sampling, sequential adaptive weighting, dynamic weighted sampling	IS, weighted Monte Carlo, Önem Örneklemesi
相关	6	5
摘要	Adaptive weighted sampling is a probabilistic sampling procedure that assigns and iteratively updates inclusion weights for population units based on observed data collected during the sampling process itself. Unlike static weighted sampling — where weights are fixed before data collection from known auxiliary information — adaptive weighting revises probabilities as new information accumulates, concentrating sampling effort on units that contribute most to estimating the target quantity. It is used in survey methodology, simulation studies, and rare-event estimation.	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.