विधियों की तुलना करें

	बायेसियन सामान्यीकृत रैखिक मॉडल	सामान्यीकृत रेखीय मॉडल (GLM)
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1989 (GLM); 1995 (Bayesian BDA)	1972
प्रवर्तक	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.	John A. Nelder & Robert W. M. Wedderburn
प्रकार	Bayesian regression model	Regression framework
मौलिक स्रोत	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	Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.
उपनाम	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM	GLM, generalized regression, exponential family regression, link-function model
संबंधित	6	6
सारांश	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.	The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case.