Comparer des méthodes

	Simulation de bootstrap hiérarchique	Échantillonnage de Gibbs
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1997-2008	1984
Auteur d'origine	Davison & Hinkley; Cameron, Gelbach & Miller	Stuart Geman & Donald Geman
Type	resampling simulation	MCMC sampling algorithm
Source fondatrice	Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716	Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
Alias	cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Apparentées	5	5
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.	Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.