论文标题
贝叶斯通用添加剂模型选择，包括快速变体选项
Bayesian Generalized Additive Model Selection Including a Fast Variational Option
论文作者
论文摘要
我们使用贝叶斯模型选择范例，例如最小的绝对收缩和选择操作员先验，以促进广义添加剂模型选择。我们的方法允许将连续预测变量的效果分为零，线性或非线性。精心量身定制的辅助变量的使用导致吉布斯马尔可夫链蒙特卡洛计划实际实施了该方法。此外，还获得了带有封闭形式更新的平均磁场变异算法。虽然不太准确，但这种快速的变化选项可增强对非常大的数据集的可扩展性。 R语言辅助工具在实践中使用的软件包。
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.
