方法对比
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| 马尔可夫链蒙特卡洛 (MCMC)× | 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≠ | Posterior sampling algorithm | 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≠ | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) | SEM, path analysis, latent variable modeling, causal modeling |
| 相关 | 3 | 3 |
| 摘要≠ | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. | 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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