标题： 多保真度计算机模型具有局部特征和非嵌套实验设计的贝叶斯分析：WRF模型的应用 显示英文标题

标题： Bayesian analysis of multifidelity computer models with local features and non-nested experimental designs: Application to the WRF model

摘要： 我们提出了一种多保真度贝叶斯仿真器，用于分析天气研究和预报（WRF）模型，当可用的模拟不是基于层次嵌套实验设计生成时。所提出的程序称为增强型贝叶斯树协同克里金法，从两个主要方面扩展了协同克里金法的范围。我们在多保真度设置中引入了一个二元树分区潜在过程，以考虑不同保真度级别下模型输出的非平稳性和潜在不连续性。此外，我们引入了一种高效的插补机制，使协同克里金法在非层次嵌套实验设计时可以实际实施，通过启用半共轭先验的指定来实现这一点。我们的插补策略允许设计一个高效的RJ-MCMC实现，涉及坍塌块和从条件分布的直接模拟。我们开发了蒙特卡洛递归仿真器，以一种计算可行的方式为每个保真度级别的模型输出提供完整的预测分布的蒙特卡洛代理。我们的方法在基准例子上的性能得到了展示，并与现有方法进行了比较。所提出的方法被用于分析涉及WRF模型的大规模气候建模应用。 显示英文摘要

摘要： We propose a multi-fidelity Bayesian emulator for the analysis of the Weather Research and Forecasting (WRF) model when the available simulations are not generated based on hierarchically nested experimental design. The proposed procedure, called Augmented Bayesian Treed Co-Kriging, extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity setting to account for non-stationary and potential discontinuities in the model outputs at different fidelity levels. Moreover, we introduce an efficient imputation mechanism which allows the practical implementation of co-kriging when the experimental design is non-hierarchically nested by enabling the specification of semi-conjugate priors. Our imputation strategy allows the design of an efficient RJ-MCMC implementation that involves collapsed blocks and direct simulation from conditional distributions. We develop the Monte Carlo recursive emulator which provides a Monte Carlo proxy for the full predictive distribution of the model output at each fidelity level, in a computationally feasible manner. The performance of our method is demonstrated on a benchmark example, and compared against existing methods. The proposed method is used for the analysis of a large-scale climate modeling application which involves the WRF model.