Comparer des méthodes

	Inférence bayésienne hiérarchique	Régression bayésienne
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1972 (Lindley & Smith); consolidated 1995–2013	—
Auteur d'origine	Lindley & Smith; Gelman et al.	—
Type	Bayesian multilevel model	Bayesian linear 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	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model	bayesian linear regression, probabilistic regression, bayesian regresyon
Apparentées	6	2
Résumé	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.