Bayesian calibration of computer models
Bayesian calibration of computer models
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DOI：
10.1111/1467-9868.00294
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发表时间：
2001-01-01
影响因子：
5.8
通讯作者：
O'Hagan, A
中科院分区：
文献类型：
--
作者：
Kennedy, MC;O'Hagan, A
我们考虑预测和不确定性分析的系统近似使用复杂的数学模型。这种模型，实现为计算机代码，通常是通用的，在这个意义上，通过适当选择一些模型的输入参数，代码可以用来预测系统在各种特定应用中的行为。然而，在任何特定应用中，必要参数的值可能是未知的。在这种情况下，系统在特定背景下的物理观测被用来学习未知参数。通过调整参数将模型拟合到观测数据的过程称为校准。校准通常通过特设拟合来实现，并且在校准之后，使用具有拟合输入值的模型来预测系统的未来行为。我们提出了一种贝叶斯校准技术，改进了这种传统的方法在两个方面。首先，预测考虑了所有不确定性来源，包括拟合参数的剩余不确定性。其次，他们试图纠正模型的任何不足，这是由观测数据和模型预测之间的差异所揭示的，即使是最佳拟合参数值。该方法说明了使用数据从核辐射释放在托木斯克，从一个更复杂的模拟核事故演习。
We consider prediction and uncertainty analysis for systems which are approximated using complex mathematical models. Such models, implemented as computer codes, are often generic in the sense that by a suitable choice of some of the model's input parameters the code can be used to predict the behaviour of the system in a variety of specific applications. However, in any specific application the values of necessary parameters may be unknown. In this case, physical observations of the system in the specific context are used to learn about the unknown parameters. The process of fitting the model to the observed data by adjusting the parameters is known as calibration. Calibration is typically effected by ad hoc fitting, and after calibration the model is used, with the fitted input values, to predict the future behaviour of the system. We present a Bayesian calibration technique which improves on this traditional approach in two respects. First, the predictions allow for all sources of uncertainty, including the remaining uncertainty over the fitted parameters. Second, they attempt to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best-fitting parameter values. The method is illustrated by using data from a nuclear radiation release at Tomsk, and from a more complex simulated nuclear accident exercise.