مقایسهٔ روش‌ها

	مدل اثرات مختلط (یا مدل خطی مختلط) رگرسیون معمولی را با گنجاندن هر دو اثرات ثابت	مدل اثرات مختلط بیزی (Bayesian Mixed Effects Model)
حوزه	آمار	آمار
خانواده	Regression model	Regression model
سال پیدایش	1982	1990s–2000s (modern Bayesian MCMC era)
پدیدآور	Laird & Ware	Gelman, Hill, and the broader Bayesian hierarchical modeling tradition
نوع	Mixed effects regression	Bayesian regression model
منبع بنیادین	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.	Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891
نام‌های دیگر	LME, LMM, mixed model, random effects model	Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model
مرتبط	4	5
خلاصه	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.	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.