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| Bayesian Multiple Linear Regression× | Lasso-Regression× | |
|---|---|---|
| Fachgebiet≠ | Statistik | Maschinelles Lernen |
| Familie≠ | Regression model | Machine learning |
| Entstehungsjahr≠ | 1971 | 1996 |
| Urheber≠ | Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al. | Tibshirani, R. |
| Typ≠ | Bayesian parametric regression | Regularized linear regression (L1 penalty) |
| Wegweisende Quelle≠ | 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 | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Aliasnamen | Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
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