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（如组套索先验）来促进广义加性模型选择。该方法可将连续预测变量的效应划分为零效应、线性效应或非线性效应三类。通过引入精心设计的辅助变量，我们实现了基于吉布斯采样的马尔可夫链蒙特卡洛方案以进行实际计算。此外，我们还获得了具有封闭形式更新的均值场变分算法。尽管该快速变分选项精度稍有不足，但能有效提升对极大数据集的可扩展性。配套的R语言程序包便于实际应用。