Compară metode

	Analiza factorială bayesiană	Bayesian Regression	Analiza Factorială Exploratorie (EFA)
Domeniu	Bayesian	Bayesian	Statistică
Familie	Bayesian methods	Bayesian methods	Latent structure
Anul apariției	2004	—	—
Autorul original	Lopes & West (2004) for Bayesian model assessment in factor analysis	—	—
Tip	Bayesian latent variable model	Bayesian linear model	Latent variable / dimension reduction
Sursa seminală	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
Denumiri alternative	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	bayesian linear regression, probabilistic regression, bayesian regresyon	common factor analysis, açımlayıcı faktör analizi, factor analysis
Înrudite	7	2	4
Rezumat	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.