Salīdzināt metodes

	Beiziešu diskriminējošā analīze	Lineārā diskriminantā analīze
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1964	1936
Autors	Seymour Geisser	Ronald A. Fisher
Tips	Supervised classification / Bayesian inference	Supervised classification and dimension reduction
Pirmavots	Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Citi nosaukumi	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis
Saistītās	4	4
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.	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.