Comparer des méthodes

	Inférence variationnelle hiérarchique	Inférence bayésienne hiérarchique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2016	1972 (Lindley & Smith); consolidated 1995–2013
Auteur d'origine	Ranganath, Altosaar, Tran & Blei	Lindley & Smith; Gelman et al.
Type	Bayesian approximate inference	Bayesian multilevel model
Source fondatrice	Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333.	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	HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Apparentées	5	6
Résumé	Hierarchical variational inference (HVI) extends standard variational inference by placing a richer, hierarchical structure on the variational family itself. Instead of using a simple mean-field approximation, HVI introduces auxiliary latent variables that capture dependencies among the main latent variables, yielding tighter evidence lower bounds and more accurate posterior approximations for complex Bayesian models.	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.