Сравнение методов

	Байесовский дискриминантный анализ	Байесовский анализ латентных классов (BLCA)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	1964	1990s–2000s
Автор метода	Seymour Geisser	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
Тип	Supervised classification / Bayesian inference	Bayesian latent variable / finite mixture model
Основополагающий источник	Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
Другие названия	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Связанные	4	6
Сводка	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 latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.