Selecting Parameters for Bayesian Calibration of a Process-Based Model: A Methodology Based on Canonical Correlation Analysis
Selecting Parameters for Bayesian Calibration of a Process-Based Model: A Methodology Based on Canonical Correlation Analysis
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DOI：
10.1137/120891344
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
F. Minunno;M. Oijen;D. Cameron;J. S. Pereira
中科院分区：
文献类型：
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作者：
F. Minunno;M. Oijen;D. Cameron;J. S. Pereira
由于计算机和基于抽样的参数估计技术的发展，贝叶斯统计在环境科学中变得越来越常见。然而，贝叶斯方法在森林研究中的应用仍然受到限制，特别是对于参数较多的模型。一些研究使用参数筛选来使计算昂贵的模型的校准成为可能。本文介绍了一种新的基于典型相关分析的参数筛选方法。此外，我们还展示了参数筛选如何影响基于过程的模型的性能。这里提出的方法可以普遍应用，特别适合于复杂的基于过程的模型，因为它对计算要求不高，而且很容易实现。它提供了关于模型所有输出的总体排名，而不是一次分析一个模型输出变量的常见敏感性方法。我们发现，参数筛选可以用来对红外线进行检测。
Bayesian statistics is becoming increasingly common in the environmental sciences because of developments in computers and sampling-based techniques for parameter estimation. However, the use of the Bayesian approach is still limited in forest research, especially for models with many parameters. Some studies have used parameter screening to make the calibration of a computationally expensive model possible. In this paper we introduce a new methodology for parameter screening, based on canonical correlation analysis. Furthermore we show how parameter screening impacts the performance of a process-based model. The methodology presented here can be generally applied and is particularly suitable for complex process-based models because it is not computationally demanding and is easy to implement. It provides an overall ranking in relation to all outputs of the model, as opposed to common sensitivity methods that analyze one model output variable at a time. We found that parameter screening can be used to red...