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Sieć bayesowska×Konfirmacyjna Analiza Czynnikowa (CFA)×Eksploracyjna analiza czynnikowa (EFA)×
DziedzinaStatystyka bayesowskaStatystykaStatystyka
RodzinaBayesian methodsLatent structureLatent structure
Rok powstania19881969
TwórcaJudea PearlKarl Jöreskog
TypProbabilistic graphical modelConfirmatory latent variable modelLatent variable / dimension reduction
Źródło pierwotnePearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363Fabrigar, 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. DOI ↗
Inne nazwyBayes network, belief network, probabilistic graphical model, directed graphical modelDoğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement modelcommon factor analysis, açımlayıcı faktör analizi, factor analysis
Pokrewne444
PodsumowanieA 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.
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ScholarGatePorównaj metody: Bayesian Network · CFA · EFA. Pobrano 2026-06-15 z https://scholargate.app/pl/compare