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| Các kỹ thuật giảm phương sai cho mô phỏng Monte Carlo× | Mô phỏng Monte Carlo× | |
|---|---|---|
| Lĩnh vực≠ | Mô phỏng | Ra quyết định |
| Họ≠ | Process / pipeline | MCDM |
| Năm ra đời≠ | 1950s–1980s (technique family) | 1949 |
| Người khởi xướng≠ | Hammersley & Morton (antithetic variates, 1956); Lavenberg & Welch (control variates, 1981); importance sampling roots in Kahn & Marshall (1953) | Metropolis, N., Ulam, S. |
| Loại≠ | Simulation variance-reduction technique family | Robustness wrapper — Monte Carlo uncertainty propagation |
| Công trình gốc≠ | Ross, S.M. (2012). Simulation (5th ed.). Academic Press. ISBN: 978-0124158252 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Tên gọi khác≠ | antithetic variates, control variates, importance sampling, stratified sampling MC | — |
| Liên quan≠ | 4 | 0 |
| Tóm tắt≠ | Variance reduction techniques are a family of methods that improve the efficiency of Monte Carlo simulation by achieving the same estimation accuracy with fewer random draws. Developed incrementally from the 1950s onward — with antithetic variates attributed to Hammersley and Morton, control variates formalised by Lavenberg and Welch, and importance sampling rooted in Kahn and Marshall — the family includes antithetic variates (AV), control variates (CV), importance sampling (IS), and stratification, each exploiting a different structural property of the target quantity to lower estimator variance without introducing bias. | 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. |
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