Regression modelRegression / GLM
Bayesian Generalized Linear Model
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.
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Sources
- 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
- McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman & Hall. ISBN: 978-0412317606
Related methods
Referenced by
Bayesian Cox RegressionBayesian Generalized additive modelBayesian Mixed Effects ModelBayesian Multinomial Logistic RegressionBayesian Multiple linear regressionBayesian Negative Binomial RegressionBayesian Ordinal Logistic RegressionBayesian Poisson RegressionBayesian Probit modelBayesian Quantile RegressionBayesian Robust RegressionBayesian Simple linear regressionBayesian Survival regressionBayesian Tobit ModelBayesian Zero-inflated model