Bayesian methods
贝叶斯因子分析
贝叶斯因子分析是一种概率潜在变量方法，它对因子载荷矩阵和残差方差施加先验分布，然后从观测数据中推断这些参数的完整后验分布。由 Lopes 和 West (2004) 在贝叶斯框架下提出，它通过量化每个估计载荷的不确定性，而不是报告单一的点估计，从而扩展了经典的探索性和验证性因子分析。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67. link ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Factor Analysis. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-factor-analysis
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.
- 贝叶斯网络贝叶斯↔ compare
- Bayesian Regression贝叶斯↔ compare
- 验证性因子分析 (CFA)统计学↔ compare
- 探索性因子分析（EFA）统计学↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 主成分分析机器学习↔ compare
- 结构方程模型 (SEM)统计学↔ compare