Bayesian Regression Modeling with Interactions and Smooth Effects
提出一种基于样条的灵活贝叶斯回归方法，用简洁方式建模平滑的双变量交互作用，并通过惩罚复杂度的先验分布实现模型选择或平均，在计算、解释和预测性能上表现良好。
Abstract There have been many recent suggestions as to how to build and estimate flexible Bayesian regression models, using constructs such as trees, neural networks, and Gaussian processes. Although there is much to commend these methods, their implementation and interpretation can be daunting for practitioners. This article presents a spline-based methodology for flexible Bayesian regression that is quite simple in terms of computation and interpretation. Smooth bivariate interactions are modeled in an economical and apparently novel way, and prior distributions that penalize complexity are used. Predictions can be based on either model selection or model averaging. Taking computation, interpretation, and predictive performance into account, the method is seen to perform well when applied to simulated and real data.