Bayesian methods
贝叶斯结构方程模型 (BSEM)
贝叶斯SEM，由Muthén和Asparouhov于2012年提出，通过对因子载荷、路径系数和协方差设置先验分布，扩展了经典的结构方程模型。它不返回单一的最大似然估计值，而是使用马尔可夫链蒙特卡洛方法为每个参数生成完整的后验分布，从而在具有潜在变量的模型中实现原则性的不确定性量化。
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来源
- Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335. link ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Structural Equation Modeling. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-sem
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯分层模型贝叶斯↔ compare
- Bayesian Regression贝叶斯↔ compare
- 验证性因子分析 (CFA)统计学↔ compare
- 潜增长曲线模型 (LGC)统计学↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 结构方程模型 (SEM)统计学↔ compare