Módszerek összehasonlítása

	Bayes-háló	Konfirmatív Faktoranalízis (CFA)	Feltáró Faktoranalízis (EFA)
Tudományterület	Bayes-statisztika	Statisztika	Statisztika
Módszercsalád	Bayesian methods	Latent structure	Latent structure
Keletkezés éve	1988	1969	—
Megalkotó	Judea Pearl	Karl Jöreskog	—
Típus	Probabilistic graphical model	Confirmatory latent variable model	Latent variable / dimension reduction
Alapmű	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	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.
Alternatív nevek	Bayes network, belief network, probabilistic graphical model, directed graphical model	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	common factor analysis, açımlayıcı faktör analizi, factor analysis
Kapcsolódó	4	4	4
Összefoglaló	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.	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.	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.