Comparer des méthodes

	Analyse discriminante bayésienne	Analyse de classification bayésienne
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1964	1998–2002
Auteur d'origine	Seymour Geisser	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
Type	Supervised classification / Bayesian inference	Probabilistic / model-based clustering
Source fondatrice	Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
Alias	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
Apparentées	4	6
Résumé	Bayesian discriminant analysis assigns observations to predefined groups by combining a multivariate Gaussian likelihood for each class with prior distributions over the class means and covariance matrices. Posterior predictive probabilities replace point-estimate decision boundaries, providing principled uncertainty quantification for classification in small or high-dimensional samples.	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.