পদ্ধতির তুলনা করুন

	বেয়েশীয় বহুচলক রৈখিক রিগ্রেশন (Bayesian Multiple Linear Regression)	বেয়েশীয় সাধারণীকৃত রৈখিক মডেল (Bayesian Generalized Linear Model)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1971	1989 (GLM); 1995 (Bayesian BDA)
প্রবর্তক	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
ধরন	Bayesian parametric regression	Bayesian regression model
মৌলিক উৎস	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	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
অপর নাম	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
সম্পর্কিত	6	6
সারসংক্ষেপ	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.