Salīdzināt metodes

	Beiziešu diskriminējošā analīze	Bayesiskā klasteru analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1964	1998–2002
Autors	Seymour Geisser	Fraley & Raftery (model-based); Dirichlet process formulations by Ferguson (1973) and Antoniak (1974)
Tips	Supervised classification / Bayesian inference	Probabilistic / model-based clustering
Pirmavots	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.
Citi nosaukumi	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	BCA, Bayesian clustering, probabilistic cluster analysis, Bayesian model-based clustering
Saistītās	4	6
Kopsavilkums	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.