方法对比
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| 贝叶斯多维尺度分析 (BMDS)× | 贝叶斯验证性因子分析 (BCFA)× | |
|---|---|---|
| 领域≠ | 统计学 | 心理测量学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 2001 | 2007–2012 |
| 提出者≠ | Oh & Raftery | Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov |
| 类型≠ | Bayesian latent-space dimensionality reduction | Bayesian latent variable model |
| 开创性文献≠ | 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 |
| 别名 | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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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