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| Regresja bayesowska× | Model Mieszanych Efektów× | Regularyzacja grzbietowa (Ridge Regression)× | |
|---|---|---|---|
| Dziedzina≠ | Statystyka bayesowska | Statystyka | Uczenie maszynowe |
| Rodzina≠ | Bayesian methods | Regression model | Machine learning |
| Rok powstania≠ | — | 1982 | 1970 |
| Twórca≠ | — | Laird & Ware | Hoerl, A.E. & Kennard, R.W. |
| Typ≠ | Bayesian linear model | Mixed effects regression | L2-regularized linear regression |
| Źródło pierwotne≠ | 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 | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Inne nazwy≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | LME, LMM, mixed model, random effects model | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Pokrewne≠ | 2 | 4 | 4 |
| Podsumowanie≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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