Process / pipeline
Importance Sampling — 稀有事件的方差缩减
Importance sampling 是一种蒙特卡洛方差缩减技术，它将采样分布移向感兴趣的区域——通常是稀有或极端事件——以便比原始分布更有可能抽取到信息性样本。该技术由 Herman Kahn 和 Theodore Harris 于 1951 年左右在 RAND 公司开发，它使得尾部概率估计（如 Value-at-Risk 或系统故障概率）在标准蒙特卡洛需要天文数字次运行的情况下变得可行。
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来源
- Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley. DOI: 10.1002/9781118631980 ↗
- Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer. DOI: 10.1007/978-0-387-21617-1 ↗
如何引用本页
ScholarGate. (2026, June 1). Importance Sampling (Variance Reduction Monte Carlo). ScholarGate. https://scholargate.app/zh/simulation/importance-sampling
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