Methoden vergelijken

	Bayesiaanse Clusteranalyse	Bayesiaanse Mixture Modellering
Vakgebied	Statistiek	Statistiek
Familie	Latent structure	Latent structure
Jaar van ontstaan	1998–2002	1997 (Richardson & Green Bayesian formulation)
Grondlegger	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)
Type	Probabilistic / model-based clustering	Latent-class / model-based clustering
Oorspronkelijke bron	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995
Aliassen	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture
Verwant	6	4
Samenvatting	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.	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.