পদ্ধতির তুলনা করুন

	বেয়েশীয় অনুক্রমিক রৈখিক মডেল (Bayesian Hierarchical Linear Model)	মাল্টিলেভেল মডেলিং
ক্ষেত্র	পরিসংখ্যান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2006	1992
প্রবর্তক	Gelman & Hill (2006); Raudenbush & Bryk (2002) for frequentist HLM; Bayesian treatment consolidated by Gelman et al.	Anthony Bryk and Stephen Raudenbush
ধরন	Bayesian multilevel linear model	Method
মৌলিক উৎস	Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
অপর নাম	Bayesian HLM, Bayesian multilevel linear model, Bayesian random-effects linear model, Bayes hierarchical regression	HLM, mixed-effects models, random effects models, MLM
সম্পর্কিত	5	3
সারসংক্ষেপ	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.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.