เปรียบเทียบวิธี

	แบบจำลองเบย์เซียนสำหรับผลกระทบสุ่ม	Mixed Effects Model
สาขาวิชา	เศรษฐมิติ	สถิติศาสตร์
ตระกูล	Regression model	Regression model
ปีกำเนิด	1972–1995	1982
ผู้ริเริ่ม	Lindley & Smith (1972); extended by Gelman, Rubin and colleagues	Laird & Ware
ประเภท	Bayesian hierarchical panel model	Mixed effects regression
แหล่งต้นตำรับ	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	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
ชื่อเรียกอื่น	Bayesian hierarchical model, Bayesian mixed effects model, Bayesian multilevel model, BREM	LME, LMM, mixed model, random effects model
ที่เกี่ยวข้อง	5	4
สรุป	The Bayesian random effects model combines panel-data random effects with a Bayesian prior framework, allowing unit-specific effects to be treated as draws from a population distribution whose hyperparameters are estimated from the data. This produces regularised, uncertainty-quantified estimates that borrow strength across units — particularly valuable for short panels, sparse groups, or settings where frequentist variance-component estimation is unstable.	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.