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| 계층적 부트스트랩 시뮬레이션× | 순차 몬테카를로 (Sequential Monte Carlo, SMC)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1997-2008 | 1993 (particle filter); 2006 (SMC samplers) |
| 창시자≠ | Davison & Hinkley; Cameron, Gelbach & Miller | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 유형≠ | resampling simulation | Sequential Bayesian computation |
| 원전≠ | Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716 | 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 | cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling | SMC, particle filter, sequential importance resampling, SMC sampler |
| 관련≠ | 5 | 6 |
| 요약≠ | Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of the data by resampling at each level of the hierarchy in sequence, producing a sampling distribution that correctly reflects both between-group and within-group variability. | 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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