Salīdzināt metodes

	Beiziešu hierarhiskā klasterizācija (BHC)	Bayesiskā klasteru analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	2005	1998–2002
Autors	Katherine Heller & Zoubin Ghahramani	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
Tips	Probabilistic clustering / model-based hierarchical agglomeration	Probabilistic / model-based clustering
Pirmavots	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.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
Citi nosaukumi	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
Saistītās	6	6
Kopsavilkums	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 cluster analysis assigns observations to latent groups by combining a probabilistic model of within-cluster data with prior beliefs about cluster parameters and the number of clusters. It yields posterior probabilities of cluster membership and principled uncertainty estimates, making it more transparent than classical distance-based clustering algorithms.