Comparer des méthodes

	Regroupement hiérarchique bayésien (BHC)	Bayesian Latent Class Analysis
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	2005	1990s–2000s
Auteur d'origine	Katherine Heller & Zoubin Ghahramani	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
Type	Probabilistic clustering / model-based hierarchical agglomeration	Bayesian latent variable / finite mixture model
Source fondatrice	Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 297–304. ACM.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
Alias	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Apparentées	6	6
Résumé	Bayesian hierarchical clustering is a probabilistic agglomerative algorithm that builds a tree of nested cluster merges using Bayesian model comparison at each step. Rather than minimising a geometric linkage criterion, it evaluates at every candidate merge whether the data from two clusters are better explained by a single combined model or by two separate models, yielding a statistically principled dendrogram.	Bayesian latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.