论文标题
具有信息失败的计算机模型的贝叶斯校准
Bayesian Calibration of Computer Models with Informative Failures
论文作者
论文摘要
计算机模型对实验数据的校准有许多实际困难。一种这样的并发症是一个事实，即校准输入的某些组合可能会导致代码输出数据缺乏基本属性，甚至根本没有产生输出。在许多情况下，研究人员希望或需要在分析中排除这些“失败”的可能性。我们提出了一个贝叶斯（元）模型，其中校准参数的后验分布自然排除了与失败运行的输入空间的区域。也就是说，我们定义了一个统计选择模型，以严格地将二进制分类的不相交问题和计算机模型校准介绍。我们使用来自碳捕获实验的数据证明了我们的方法，其中计算流体动力学的数字容易出现不稳定性。
There are many practical difficulties in the calibration of computer models to experimental data. One such complication is the fact that certain combinations of the calibration inputs can cause the code to output data lacking fundamental properties, or even to produce no output at all. In many cases the researchers want or need to exclude the possibility of these "failures" within their analyses. We propose a Bayesian (meta-)model in which the posterior distribution for the calibration parameters naturally excludes regions of the input space corresponding to failed runs. That is, we define a statistical selection model to rigorously couple the disjoint problems of binary classification and computer model calibration. We demonstrate our methodology using data from a carbon capture experiment in which the numerics of the computational fluid dynamics are prone to instability.
