Comparer des méthodes

	Modélisation par mélange	Modélisation bayésienne de mélanges
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1894	1997 (Richardson & Green Bayesian formulation)
Auteur d'origine	Karl Pearson	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)
Type	Latent variable / density estimation	Latent-class / model-based clustering
Source fondatrice	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
Alias	finite mixture model, mixture distribution model, FMM, model-based clustering	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture
Apparentées	6	4
Résumé	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.