方法对比

	贝叶斯广义相加模型 (Bayesian GAM)	广义可加模型 (GAM)
领域	统计学	机器学习
方法族	Regression model	Machine learning
起源年份	1990s–2000s	1986
提出者	Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others	Trevor Hastie & Robert Tibshirani
类型	Semiparametric Bayesian regression	Semi-parametric additive regression model
开创性文献	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.
别名	Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
相关	4	4
摘要	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.