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	Hierarchiczne klastrowanie bayesowskie (BHC)	Bayesowska analiza klas ukrytych (BLCA)
Dziedzina	Statystyka	Statystyka
Rodzina	Latent structure	Latent structure
Rok powstania	2005	1990s–2000s
Twórca	Katherine Heller & Zoubin Ghahramani	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
Typ	Probabilistic clustering / model-based hierarchical agglomeration	Bayesian latent variable / finite mixture model
Źródło pierwotne	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.
Inne nazwy	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Pokrewne	6	6
Podsumowanie	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.