Comparer des méthodes

	Metropolis-Hastings multiniveau	Inférence bayésienne hiérarchique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1953 (core); 1990s (multilevel application)	1972 (Lindley & Smith); consolidated 1995–2013
Auteur d'origine	Metropolis et al. (1953); hierarchical extension developed through 1980s–1990s Bayesian computation literature	Lindley & Smith; Gelman et al.
Type	MCMC sampling algorithm	Bayesian multilevel model
Source fondatrice	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	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	hierarchical Metropolis-Hastings, multilevel MH, MH for hierarchical models, blocked Metropolis-Hastings	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Apparentées	6	6
Résumé	Multilevel Metropolis-Hastings applies the Metropolis-Hastings MCMC algorithm to hierarchical (multilevel) Bayesian models, sampling jointly from group-level parameters and hyperparameters by proposing candidate values and accepting or rejecting them via a ratio that respects the full joint posterior across all levels of the model.	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.