Methoden vergelijken

	Bayesian Factor Analysis	Bayesiaans Netwerk	Exploratieve factoranalyse (EFA)
Vakgebied	Bayesiaanse statistiek	Bayesiaanse statistiek	Statistiek
Familie	Bayesian methods	Bayesian methods	Latent structure
Jaar van ontstaan	2004	1988	—
Grondlegger	Lopes & West (2004) for Bayesian model assessment in factor analysis	Judea Pearl	—
Type	Bayesian latent variable model	Probabilistic graphical model	Latent variable / dimension reduction
Oorspronkelijke bron	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	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.
Aliassen	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	Bayes network, belief network, probabilistic graphical model, directed graphical model	common factor analysis, açımlayıcı faktör analizi, factor analysis
Verwant	7	4	4
Samenvatting	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.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	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.