方法对比
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| 贝叶斯LASSO回归× | 贝叶斯岭回归× | |
|---|---|---|
| 领域≠ | 统计学 | 机器学习 |
| 方法族≠ | Regression model | Bayesian methods |
| 起源年份≠ | 2008 | 1992 |
| 提出者≠ | Park & Casella | MacKay, D. J. C. |
| 类型≠ | Bayesian regularized regression | Probabilistic regularised regression |
| 开创性文献≠ | Park, T., & Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association, 103(482), 681–686. DOI ↗ | MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. DOI ↗ |
| 别名 | Bayesian LASSO, Bayesian L1 regression, double-exponential prior regression, Laplace prior regression | BRR, Bayesian linear regression with automatic relevance determination, evidence approximation ridge, marginal likelihood ridge |
| 相关≠ | 5 | 3 |
| 摘要≠ | Bayesian LASSO regression places double-exponential (Laplace) priors on regression coefficients, which is the Bayesian analogue of the classical LASSO penalty. It simultaneously shrinks small coefficients toward zero and performs soft variable selection, all within a coherent posterior inference framework that naturally quantifies parameter uncertainty through credible intervals. | Bayesian Ridge Regression is a probabilistic formulation of ridge regression, introduced by David J. C. MacKay in 1992, in which the regularisation strength and noise precision are not fixed by the analyst but are instead estimated automatically by maximising the marginal likelihood (evidence) of the observed data. The result is a full posterior distribution over the regression weights together with calibrated predictive uncertainty. |
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