Сравнение методов

	Байесовский факторный анализ	Анализ главных компонент
Область	Байесовские методы	Машинное обучение
Семейство	Bayesian methods	Machine learning
Год появления	2004	2002
Автор метода	Lopes & West (2004) for Bayesian model assessment in factor analysis	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Тип	Bayesian latent variable model	Unsupervised dimensionality reduction
Основополагающий источник	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Другие названия	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Связанные	7	3
Сводка	Bayesian Factor Analysis is a probabilistic latent-variable method that places prior distributions on the factor loading matrix and the residual variances, then infers a full posterior over these parameters from the observed data. Developed prominently in the Bayesian framework by Lopes and West (2004), it extends classical exploratory and confirmatory factor analysis by quantifying uncertainty in every estimated loading rather than reporting single point estimates.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.