השוואת שיטות

	ניתוח גורמים בייסיאני	רשת בייסיאנית	ניתוח גורמים גישוש (EFA)
תחום	בייסיאני	בייסיאני	סטטיסטיקה
משפחה	Bayesian methods	Bayesian methods	Latent structure
שנת המקור	2004	1988	—
הוגה השיטה	Lopes & West (2004) for Bayesian model assessment in factor analysis	Judea Pearl	—
סוג	Bayesian latent variable model	Probabilistic graphical model	Latent variable / dimension reduction
מקור מכונן	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.
כינויים	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
קשורות	7	4	4
תקציר	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.