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| Bayes'sche Netzwerke× | Exploratorische Faktorenanalyse (EFA)× | |
|---|---|---|
| Fachgebiet≠ | Bayes-Statistik | Statistik |
| Familie≠ | Bayesian methods | Latent structure |
| Entstehungsjahr≠ | 1988 | — |
| Urheber≠ | Judea Pearl | — |
| Typ≠ | Probabilistic graphical model | Latent variable / dimension reduction |
| Wegweisende Quelle≠ | 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. DOI ↗ |
| Aliasnamen≠ | Bayes network, belief network, probabilistic graphical model, directed graphical model | common factor analysis, açımlayıcı faktör analizi, factor analysis |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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. |
| ScholarGateDatensatz ↗ |
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