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.

我们使用贝叶斯模型选择范例，例如组最小绝对收缩和选择算子先验，以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影响分为零、线性或非线性。使用精心定制的辅助变量会产生吉布斯马尔可夫链蒙特卡罗方案，以实际实施该方法。此外，还获得了具有封闭形式更新的平均场变分算法。虽然不那么准确，但这种快速变分选项增强了对非常大的数据集的可扩展性。 R语言的包有助于实际使用。