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：我们使用贝叶斯模型选择方法，如群组最小绝对收缩和选择算子先验，以便促进广义加性模型选择。我们的方法允许将连续预测变量的效应分类为零，线性或非线性。使用经过精心设计的辅助变量会导致 Gibbs 马尔可夫链蒙特卡罗方案，以实现该方法的实际应用。此外，可获得平均场变分算法，其中包含闭式更新。虽然不太准确，但这种快速变分选项提高了对大型数据集的可扩展性。 R 语言中的一个软件包可帮助实际应用。