Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesiešu daudzdimensionālā skalēšana (BMDS)× | Bayesiskā šķirības faktoru analīze (BCFA)× | |
|---|---|---|
| Nozare≠ | Statistika | Psihometrija |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 2001 | 2007–2012 |
| Autors≠ | Oh & Raftery | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| Tips≠ | Bayesian latent-space dimensionality reduction | Bayesian latent variable model |
| Pirmavots≠ | Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044. DOI ↗ | Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232 |
| Citi nosaukumi | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model comparison rather than heuristic inspection. | 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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