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| Бейсианска мрежа× | Конфирматорният факторен анализ (КФА)× | Експлораторният факторен анализ (EFA)× | |
|---|---|---|---|
| Област≠ | Бейсови методи | Статистика | Статистика |
| Семейство≠ | Bayesian methods | Latent structure | Latent structure |
| Година на възникване≠ | 1988 | 1969 | — |
| Създател≠ | Judea Pearl | Karl Jöreskog | — |
| Тип≠ | Probabilistic graphical model | Confirmatory latent variable model | Latent variable / dimension reduction |
| Основополагащ източник≠ | 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. DOI ↗ |
| Други названия≠ | 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 |
| Свързани | 4 | 4 | 4 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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