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| Bayesowska analiza głównych składowych (BPCA)× | Bayesowska eksploracyjna analiza czynnikowa (BEFA)× | |
|---|---|---|
| Dziedzina≠ | Statystyka | Psychometria |
| Rodzina | Latent structure | Latent structure |
| Rok powstania≠ | 1999 | 2004 (Bayesian formulation); factor analysis roots: 1904 |
| Twórca≠ | Christopher M. Bishop | Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904) |
| Typ≠ | Bayesian latent variable / dimension reduction | Probabilistic latent variable model |
| Źródło pierwotne≠ | Bishop, C. M. (1999). Bayesian PCA. In M. S. Kearns, S. A. Solla & D. A. Cohn (Eds.), Advances in Neural Information Processing Systems 11 (pp. 382–388). MIT Press. link ↗ | Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67. link ↗ |
| Inne nazwy | BPCA, Bayesian PCA, probabilistic PCA with Bayesian inference, variational Bayesian PCA | Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis |
| Pokrewne≠ | 2 | 4 |
| Podsumowanie≠ | Bayesian principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of the representation. | Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data. |
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