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| 다층 부트스트랩 시뮬레이션× | 순차 몬테카를로 (Sequential Monte Carlo, SMC)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1979 (bootstrap); multilevel variants c.1990s | 1993 (particle filter); 2006 (SMC samplers) |
| 창시자≠ | Efron (1979); multilevel extensions developed through 1980s–2000s | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 유형≠ | resampling / simulation | Sequential Bayesian computation |
| 원전≠ | Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. DOI ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113. DOI ↗ |
| 별칭 | hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling | SMC, particle filter, sequential importance resampling, SMC sampler |
| 관련 | 6 | 6 |
| 요약≠ | Multilevel bootstrap simulation is a resampling technique designed for clustered or hierarchically structured data. It preserves the nested data structure by resampling at each level independently — first drawing clusters (e.g., schools, hospitals), then drawing observations within each sampled cluster — so that bootstrap replicate datasets reflect the same multilevel organisation as the original data. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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