Assessing parameter uncertainty on coupled models using minimum information methods
Assessing parameter uncertainty on coupled models using minimum information methods
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DOI：
10.1016/j.ress.2013.05.011
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发表时间：
2014-05-01
影响因子：
8.1
通讯作者：
Daneshkhah, Alireza
中科院分区：
文献类型：
--
作者：
Bedford, Tim;Wilson, Kevin J.;Daneshkhah, Alireza
概率反演用于对可观察的模型输出进行专家不确定性评估，并从这些评估中构建模型参数的分布，该分布捕获专家表达的不确定性。在本文中，我们看看如何使用最小信息的方法来做到这一点，特别是集中在确保不同变量的专家评估之间的一致性的问题，无论是从一个单一的模型输出或潜在的输出沿着一个模型链。本文展示了如何构造这样的问题，然后用两个例子说明了该方法;一个涉及串联系统中设备的故障率，另一个涉及大气扩散和沉积。(C)2013由Elsevier Ltd.出版
Probabilistic inversion is used to take expert uncertainty assessments about observable model outputs and build from them a distribution on the model parameters that captures the uncertainty expressed by the experts. In this paper we look at ways to use minimum information methods to do this, focussing in particular on the problem of ensuring consistency between expert assessments about differing variables, either as outputs from a single model or potentially as outputs along a chain of models. The paper shows how such a problem can be structured and then illustrates the method with two examples; one involving failure rates of equipment in series systems and the other atmospheric dispersion and deposition. (C) 2013 Published by Elsevier Ltd.