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Statistical Methods for Uncertainty Analysis and Sensitivity Analysis Associated with Computer Models

机译：与计算机模型相关的不确定性分析和灵敏度分析的统计方法

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A computer model for a physical process produces a time dependent output Z based on a particular selection of input values X/sub l/, ..., X/sub k/. For the case considered herein it is required to make a number of computer runs n and use these results to estimate the distribution function for the output Z and to quantify the uncertainty as reflected by this distribution function due to variation in the input. It is also important to assess the influence of the input variables and determine which are dominent. Earmarks of such computer models for physical processes are complexity both in terms of the number of input variables and the level of mathematics. The complexity of the mathematics (usually a series of differential equations) precludes a simple analytic solution for identification of the important or dominant variables. Also, the large number of input variables and the long periods of time which the models span act toether to greatly increase the amount of computer time required to make a single run of the computer model. These requirements impose some limitations on the number of computer runs that can be made and therefore a judicious selection of specific values of input variables is necessitated. this paper presents a discussion of the selection of the input values for a computer model and the subsequent analysis of the output. The discussion will include examples which are based on a methodology developed at Sandia National Laboratories for the NRC to assess the risk associated with the geologic isolation of high level radioactive wastes. (ERA citation 07:048941)

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