Porównaj metody

	Symulacja metodą wielopoziomowego bootstrapu	Sekwencyjne metody Monte Carlo
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1979 (bootstrap); multilevel variants c.1990s	1993 (particle filter); 2006 (SMC samplers)
Twórca	Efron (1979); multilevel extensions developed through 1980s–2000s	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Typ	resampling / simulation	Sequential Bayesian computation
Źródło pierwotne	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.
Inne nazwy	hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling	SMC, particle filter, sequential importance resampling, SMC sampler
Pokrewne	6	6
Podsumowanie	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.