مقایسهٔ روش‌ها

	مدل افزایشی تعمیم‌یافته بیزی (Bayesian GAM)	مدل جمعی تعمیم‌یافته (GAM)
حوزه	آمار	یادگیری ماشین
خانواده	Regression model	Machine learning
سال پیدایش	1990s–2000s	1986
پدیدآور	Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others	Trevor Hastie & Robert Tibshirani
نوع	Semiparametric Bayesian regression	Semi-parametric additive regression model
منبع بنیادین	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.
نام‌های دیگر	Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
مرتبط	4	4
خلاصه	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.