Screening traditionally refers to the problem of detecting active inputs in the computer model. In this paper, we develop methodology that applies to screening, but the main focus is on detecting active inputs not in the computer model itself but rather on the discrepancy function that is introduced to account for model inadequacy when linking the computer model with field observations. We contend this is an important problem as it informs the modeler which are the inputs that are potentially being mishandled in the model, but also along which directions it may be less recommendable to use the model for prediction. The methodology is Bayesian and is inspired by the continuous spike and slab prior popularized by the literature on Bayesian variable selection. In our approach, and in contrast with previous proposals, a single MCMC sample from the full model allows us to compute the posterior probabilities of all the competing models, resulting in a methodology that is computationally very fast. The approach hinges on the ability to obtain posterior inclusion probabilities of the inputs, which are very intuitive and easy to interpret quantities, as the basis for selecting active inputs. For that reason, we name the methodology PIPS -- posterior inclusion probability screening.

翻译：筛选传统上指的是检测计算机模型中活跃输入的问题。本文开发了一种适用于筛选的方法，但主要关注点并非计算机模型本身，而是在连接计算机模型与现场观测时引入的差异函数中检测活跃输入。我们认为这是一个重要问题，因为它向建模者揭示了模型中可能被错误处理的输入，同时也能指示沿哪些方向使用该模型进行预测可能不太可取。该方法基于贝叶斯框架，其灵感来自贝叶斯变量选择文献中广泛使用的连续尖峰-板先验。与以往方法不同，我们的方法通过从完整模型中进行一次马尔可夫链蒙特卡洛采样，即可计算所有竞争模型的后验概率，从而实现了极高的计算效率。该方法的成功依赖于能够获得输入的后验包含概率——这一直观且易于解释的量化指标——作为筛选活跃输入的基础。基于此，我们将该方法命名为PIPS——后验包含概率筛选。