Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская обобщенная аддитивная модель (Bayesian GAM)× | Обобщенная аддитивная модель (GAM)× | |
|---|---|---|
| Область≠ | Статистика | Машинное обучение |
| Семейство≠ | Regression model | Machine learning |
| Год появления≠ | 1990s–2000s | 1986 |
| Автор метода≠ | Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others | Trevor Hastie & Robert Tibshirani |
| Тип≠ | Semiparametric Bayesian regression | Semi-parametric additive regression model |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model |
| Связанные | 4 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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