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	Symulacja metodą wielopoziomowego bootstrapu	Hierarchiczna inferencja bayesowska
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1979 (bootstrap); multilevel variants c.1990s	1972 (Lindley & Smith); consolidated 1995–2013
Twórca	Efron (1979); multilevel extensions developed through 1980s–2000s	Lindley & Smith; Gelman et al.
Typ	resampling / simulation	Bayesian multilevel model
Źródło pierwotne	Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Inne nazwy	hierarchical bootstrap, cluster bootstrap, stratified bootstrap for multilevel data, multilevel resampling	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
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.	Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.