方法对比
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| 贝叶斯混合效应模型× | 混合效应模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1990s–2000s (modern Bayesian MCMC era) | 1982 |
| 提出者≠ | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition | Laird & Ware |
| 类型≠ | Bayesian regression model | Mixed effects regression |
| 开创性文献≠ | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ |
| 别名 | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model | LME, LMM, mixed model, random effects model |
| 相关≠ | 5 | 4 |
| 摘要≠ | The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. |
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