Comparer des méthodes

	Simulation de bootstrap hiérarchique	Inférence bayésienne hiérarchique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1997-2008	1972 (Lindley & Smith); consolidated 1995–2013
Auteur d'origine	Davison & Hinkley; Cameron, Gelbach & Miller	Lindley & Smith; Gelman et al.
Type	resampling simulation	Bayesian multilevel model
Source fondatrice	Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716	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
Alias	cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Apparentées	5	6
Résumé	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.	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.