Сравнение методов

	Многоуровневое бутстреп-моделирование	Последовательный Монте-Карло
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1979 (bootstrap); multilevel variants c.1990s	1993 (particle filter); 2006 (SMC samplers)
Автор метода	Efron (1979); multilevel extensions developed through 1980s–2000s	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Тип	resampling / simulation	Sequential Bayesian computation
Основополагающий источник	Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.	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.
Другие названия	hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling	SMC, particle filter, sequential importance resampling, SMC sampler
Связанные	6	6
Сводка	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.