Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models
Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models
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DOI：
10.1137/120900915
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发表时间：
2014-01
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影响因子：
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通讯作者：
D. Williamson;A. Blaker
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文献类型：
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作者：
D. Williamson;A. Blaker
我们开发了贝叶斯动态线性模型高斯过程仿真的时间序列输出的计算机模型，可能会表现出混乱的行为，但这种行为保留了一些基本的结构。统计技术特别适合于模拟大型气候模型的时间序列输出，这些模型具有这种功能，我们希望模拟器后部的样本以与计算机模型中的动态过程相同的方式演变。该方法结合了良好的不确定性量化（UQ）方法的关键功能，如使用复杂的均值函数来捕获参数空间内的大规模信号，与动态线性模型的方式，允许UQ借用强度从贝叶斯时间序列文献。当高斯过程的粗糙度长度未知时，我们提出了一种从仿真器参数的后验采样的MCMC算法。我们讨论的解释，该算法的结果，使我们能够使用MCMC来修复…
We develop Bayesian dynamic linear model Gaussian processes for emulation of time series output for computer models that may exhibit chaotic behavior, but where this behavior retains some underlying structure. The statistical technology is particularly suited to emulating the time series output of large climate models that exhibit this feature and where we want samples from the posterior of the emulator to evolve in the same way as dynamic processes in the computer model do. The methodology combines key features of good uncertainty quantification (UQ) methods such as using complex mean functions to capture large-scale signals within parameter space, with dynamic linear models in a way that allows UQ to borrow strength from the Bayesian time series literature. We present an MCMC algorithm for sampling from the posterior of the emulator parameters when the roughness lengths of the Gaussian process are unknown. We discuss an interpretation of the results of this algorithm that allows us to use MCMC to fix th...