Methoden vergleichen

	Bayesian Factor Analysis	Bayes'sche Regression	Bestätigende Faktorenanalyse (CFA)
Fachgebiet	Bayes-Statistik	Bayes-Statistik	Statistik
Familie	Bayesian methods	Bayesian methods	Latent structure
Entstehungsjahr	2004	—	1969
Urheber	Lopes & West (2004) for Bayesian model assessment in factor analysis	—	Karl Jöreskog
Typ	Bayesian latent variable model	Bayesian linear model	Confirmatory latent variable model
Wegweisende Quelle	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	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363
Aliasnamen	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	bayesian linear regression, probabilistic regression, bayesian regresyon	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model
Verwandt	7	2	4
Zusammenfassung	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.	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.