On the Statistical Formalism of Uncertainty Quantification
On the Statistical Formalism of Uncertainty Quantification
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DOI：
10.1146/annurev-statistics-030718-105232
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发表时间：
2019-01-01
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影响因子：
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通讯作者：
Smith, Leonard A.
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文献类型：
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作者：
Berger, James O.;Smith, Leonard A.
使用模型试图更好地理解现实是无处不在的。事实证明，模型在测试我们目前对现实的理解方面是有用的；例如，20世纪80年代的气候模型是为了科学发现而建立的，以更好地理解气候系统的一般动力学。科学的洞察力通常采取一般定性预测的形式(即，在这些条件下，地球两极将比地球其他地方变暖更多)；这种模型的使用不同于对特定事件的定量预测(即“明天中午伦敦希思罗机场的大风”)。人们有时希望，在充分的模型开发之后，任何模型都可以用来对任何目标系统进行定量预测。即使是这样，预测中也总会有一些不确定性。不确定性量化旨在提供一个框架，在这个框架内可以讨论不确定性，最好是以与使用预报系统的从业人员相关的方式进行量化。一种统计形式主义已经发展起来，声称能够准确评估预测中的不确定性。本文讨论的是这种形式主义是否以及何时可以做到这一点。这篇文章源于两位作者关于这一问题的持续讨论，第二位作者通常对形式主义在提供与决策相关的量化信息方面的效用持相当大的怀疑态度。
The use of models to try to better understand reality is ubiquitous. Models have proven useful in testing our current understanding of reality; for instance, climate models of the 1980s were built for science discovery, to achieve a better understanding of the general dynamics of climate systems. Scientific insights often take the form of general qualitative predictions (i.e., "under these conditions, the Earth's poles will warm more than the rest of the planet"); such use of models differs from making quantitative forecasts of specific events (i.e. "high winds at noon tomorrow at London's Heathrow Airport"). It is sometimes hoped that, after sufficient model development, any model can be used to make quantitative forecasts for any target system. Even if that were the case, there would always be some uncertainty in the prediction. Uncertainty quantification aims to provide a framework within which that uncertainty can be discussed and, ideally, quantified, in a manner relevant to practitioners using the forecast system. A statistical formalism has developed that claims to be able to accurately assess the uncertainty in prediction. This article is a discussion of if and when this formalism can do so. The article arose from an ongoing discussion between the authors concerning this issue, the second author generally being considerably more skeptical concerning the utility of the formalism in providing quantitative decision-relevant information.