So sánh phương pháp

	Mô hình hóa hỗn hợp	Mô hình hóa hỗn hợp Bayes
Lĩnh vực	Thống kê	Thống kê
Họ	Latent structure	Latent structure
Năm ra đời	1894	1997 (Richardson & Green Bayesian formulation)
Người khởi xướng	Karl Pearson	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)
Loại	Latent variable / density estimation	Latent-class / model-based clustering
Công trình gốc	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995
Tên gọi khác	finite mixture model, mixture distribution model, FMM, model-based clustering	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture
Liên quan	6	4
Tóm tắt	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.	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.