So sánh phương pháp

	Suy luận Bayes phân cấp	Mô hình hiệu ứng hỗn hợp
Lĩnh vực	Bayes	Thống kê
Họ	Bayesian methods	Regression model
Năm ra đời	1972 (Lindley & Smith); consolidated 1995–2013	1982
Người khởi xướng	Lindley & Smith; Gelman et al.	Laird & Ware
Loại	Bayesian multilevel model	Mixed effects regression
Công trình gốc	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.
Tên gọi khác	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model	LME, LMM, mixed model, random effects model
Liên quan	6	4
Tóm tắt	Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.	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.