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| Model Linier Umum Bayesian× | Regresi Linier Berganda Bayesian× | |
|---|---|---|
| Bidang | Statistika | Statistika |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 1989 (GLM); 1995 (Bayesian BDA) | 1971 |
| Pencetus≠ | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. | Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al. |
| Tipe≠ | Bayesian regression model | Bayesian parametric regression |
| Sumber perintis | 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 |
| Alias | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM | Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression |
| Terkait | 6 | 6 |
| Ringkasan≠ | 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. | 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. |
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