เปรียบเทียบวิธี

	Bayesian Hierarchical Linear Model	Mixed Effects Model
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์
ตระกูล	Regression model	Regression model
ปีกำเนิด	2006	1982
ผู้ริเริ่ม	Gelman & Hill (2006); Raudenbush & Bryk (2002) for frequentist HLM; Bayesian treatment consolidated by Gelman et al.	Laird & Ware
ประเภท	Bayesian multilevel linear model	Mixed effects regression
แหล่งต้นตำรับ	Gelman, A., & Hill, J. (2006). 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.
ชื่อเรียกอื่น	Bayesian HLM, Bayesian multilevel linear model, Bayesian random-effects linear model, Bayes hierarchical regression	LME, LMM, mixed model, random effects model
ที่เกี่ยวข้อง	5	4
สรุป	The Bayesian Hierarchical Linear Model (Bayesian HLM) estimates linear relationships in nested or clustered data by placing prior distributions on all model parameters and updating them with observed data. It simultaneously models variation within groups and between groups, propagating uncertainty fully through posterior distributions rather than relying on asymptotic approximations.	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.