We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach allows for the effects of continuous predictors to be categorized as either zero, linear or non-linear. Employment of carefully tailored auxiliary variables results in Gibbsian Markov chain Monte Carlo schemes for practical implementation of the approach. In addition, mean field variational algorithms with closed form updates are obtained. Whilst not as accurate, this fast variational option enhances scalability to very large data sets. A package in the R language aids use in practice.

翻译：我们采用贝叶斯模型选择范式（如组LASSO先验）来促进广义加性模型的选择。该方法允许将连续预测变量的效应归为零效应、线性效应或非线性效应。通过引入精心设计的辅助变量，我们获得了可实际实施的吉布斯马尔可夫链蒙特卡洛方案。此外，还推导出具有闭式更新的平均场变分算法。尽管精度有所降低，但这种快速变分选项增强了对超大数据集的可扩展性。一个R语言软件包有助于实际应用。