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| Analisi Discriminante Bayesiana× | Analisi Fattoriale Confermativa Bayesiana (BCFA)× | |
|---|---|---|
| Campo≠ | Statistica | Psicometria |
| Famiglia | Latent structure | Latent structure |
| Anno di origine≠ | 1964 | 2007–2012 |
| Ideatore≠ | Seymour Geisser | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Tipo≠ | Supervised classification / Bayesian inference | Bayesian latent variable model |
| Fonte seminale≠ | Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76. link ↗ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| Alias | BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Correlati | 4 | 4 |
| Sintesi≠ | Bayesian discriminant analysis assigns observations to predefined groups by combining a multivariate Gaussian likelihood for each class with prior distributions over the class means and covariance matrices. Posterior predictive probabilities replace point-estimate decision boundaries, providing principled uncertainty quantification for classification in small or high-dimensional samples. | Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally. |
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