方法对比
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| Bayesian Regression× | 结构方程模型× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 研究统计学 |
| 方法族≠ | Bayesian methods | Process / pipeline |
| 起源年份≠ | — | 1921 |
| 提出者≠ | — | Sewall Wright |
| 类型≠ | Bayesian linear model | Method |
| 开创性文献≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Jöreskog, K. G., & Sörbom, D. (1973). LISREL: A general computer program for estimating a linear structural equation system. Research Bulletin 73-5. University of Stockholm. link ↗ |
| 别名≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | SEM, path analysis, latent variable modeling, causal modeling |
| 相关≠ | 2 | 3 |
| 摘要≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | Structural equation modeling (SEM) is a comprehensive statistical framework combining path analysis (Sewall Wright, 1921) and confirmatory factor analysis to test complex causal models linking observed and latent variables. Formalized by Jöreskog (1973) with LISREL software, SEM enables simultaneous estimation of measurement relationships (how variables measure latent constructs) and structural relationships (how constructs influence outcomes), making it powerful for theory testing in psychology, epidemiology, organizational research, and health sciences where complex mediation, moderation, and latent processes require integrated analysis. |
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