AI 中文总结
研究如何用非参数技术校准计算机模型，输入含校准参数和控制参数。基于测度分解和贝叶斯推理，给出与观测数据一致且保留控制参数分布的输入空间分布估计。
AI 中文摘要
我们提出一种使用非参数技术校准计算机模型的方法，其中输入是随机的，包括分布未知的校准参数和分布已指定的控制参数。我们的解决方案在输入空间上给出与观测场数据一致的分布估计，同时保留控制参数已知边缘分布。该属性很理想，因为随机输入常包含影响实验条件的物理过程，科学合理的校准估计应保留这些输入既定的分布属性。该方法基于最近基于测度分解和贝叶斯推理发展的非参数计算机模型校准技术。
英文摘要
We present a method for calibrating a computer model using non-parametric techniques where the inputs are stochastic but include calibration parameters whose distributions are unknown and control parameters whose distributions are specified. Our solution gives a distributional estimate over the input space that is consistent with observed field data, while also preserving the distribution of the known marginal of the control parameters. This property is desirable since stochastic inputs often include physical processes affecting the experimental conditions, and a scientifically plausible calibration estimate should preserve well-established distributional properties of these inputs. The method builds on recently developed non-parametric computer model calibration techniques based on the disintegration of measure and Bayesian inference.
Comments19 pages, 7 figures
