Porównaj metody

	Symulacja metodą wielopoziomowego bootstrapu	Wielopoziomowe wnioskowanie wariacyjne
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1979 (bootstrap); multilevel variants c.1990s	2016
Twórca	Efron (1979); multilevel extensions developed through 1980s–2000s	Ranganath, Altosaar, Tran, Blei (hierarchical VI formalization, 2016); Blei et al. (VI framework, 2017)
Typ	resampling / simulation	approximate Bayesian inference
Źródło pierwotne	Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.	Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.
Inne nazwy	hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling	hierarchical variational inference, multilevel VI, variational Bayes for multilevel models, MLVI
Pokrewne	6	4
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.	Multilevel variational inference (MLVI) is a scalable approximate Bayesian method that fits hierarchical (multilevel) models by optimizing a variational approximation to the posterior, rather than drawing MCMC samples. It exploits the grouped structure of multilevel data — individuals nested within groups, groups nested within higher-level units — to derive efficient coordinate-wise updates, making Bayesian inference tractable for large clustered datasets.