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| Il Modello Additivo Generalizzato Bayesiano (Bayesian GAM)× | Modello Additivo Generalizzato (GAM)× | |
|---|---|---|
| Campo≠ | Statistica | Apprendimento automatico |
| Famiglia≠ | Regression model | Machine learning |
| Anno di origine≠ | 1990s–2000s | 1986 |
| Ideatore≠ | Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others | Trevor Hastie & Robert Tibshirani |
| Tipo≠ | Semiparametric Bayesian regression | Semi-parametric additive regression model |
| Fonte seminale≠ | Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. ISBN: 9781498728331 | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ |
| Alias | Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model |
| Correlati | 4 | 4 |
| Sintesi≠ | Bayesian Generalized Additive Models extend the frequentist GAM framework by placing prior distributions over the smooth functions and any additional model parameters. This yields full posterior distributions over each smooth effect, enabling principled uncertainty quantification, automatic smoothness selection via hyperpriors, and seamless integration with hierarchical or mixed-effects structures. | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. |
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