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贝叶斯广义相加模型 (Bayesian GAM)

贝叶斯广义相加模型 (Bayesian GAM) 通过对平滑函数和任何额外模型参数设置先验分布，扩展了频率论的GAM框架。这产生了每个平滑效应的完整后验分布，从而实现了原则性的不确定性量化、通过超先验进行自动平滑度选择，以及与分层或混合效应结构的无缝集成。

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贝叶斯广义相加模型 (Bayesian GAM)
贝叶斯广义线性模型贝叶斯混合效应模型贝叶斯多元线性回归广义可加模型 (GAM)

来源

  1. Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. ISBN: 9781498728331
  2. Bürkner, P.-C. (2017). brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28. DOI: 10.18637/jss.v080.i01 ↗

如何引用本页

ScholarGate. (2026, June 3). Bayesian Generalized Additive Model. ScholarGate. https://scholargate.app/zh/statistics/bayesian-generalized-additive-model

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ScholarGate — Bayesian Generalized additive model (Bayesian Generalized Additive Model). 于 2026-06-15 检索自 https://scholargate.app/zh/statistics/bayesian-generalized-additive-model · 数据集： https://doi.org/10.5281/zenodo.20539026