So sánh phương pháp

	Phân cụm phân cấp Bayes (BHC)	Mô hình hóa hỗn hợp Bayes
Lĩnh vực	Thống kê	Thống kê
Họ	Latent structure	Latent structure
Năm ra đời	2005	1997 (Richardson & Green Bayesian formulation)
Người khởi xướng	Katherine Heller & Zoubin Ghahramani	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)
Loại	Probabilistic clustering / model-based hierarchical agglomeration	Latent-class / model-based clustering
Công trình gốc	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.	Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995
Tên gọi khác	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture
Liên quan	6	4
Tóm tắt	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 mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifying uncertainty over latent group assignments rather than treating them as fixed.