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| 베이즈 혼합 효과 모형× | Mixed Effects Model× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1990s–2000s (modern Bayesian MCMC era) | 1982 |
| 창시자≠ | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition | Laird & Ware |
| 유형≠ | Bayesian regression model | Mixed effects regression |
| 원전≠ | 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 ↗ |
| 별칭 | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model | LME, LMM, mixed model, random effects model |
| 관련≠ | 5 | 4 |
| 요약≠ | 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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