Laboratoire de Chimie Physique, UMR 8000, CNRS/Université Paris-Sud, F-91405 Orsay, France.
J Chem Phys. 2017 Sep 14;147(10):104102. doi: 10.1063/1.4994654.
Statistical estimation of the prediction uncertainty of physical models is typically hindered by the inadequacy of these models due to various approximations they are built upon. The prediction errors caused by model inadequacy can be handled either by correcting the model's results or by adapting the model's parameter uncertainty to generate prediction uncertainties representative, in a way to be defined, of model inadequacy errors. The main advantage of the latter approach (thereafter called PUI, for Parameter Uncertainty Inflation) is its transferability to the prediction of other quantities of interest based on the same parameters. A critical review of implementations of PUI in several areas of computational chemistry shows that it is biased, in the sense that it does not produce prediction uncertainty bands conforming to model inadequacy errors.
统计预测不确定性的物理模型通常受到阻碍的不足，这些模型由于各种近似他们是建立在。预测误差引起的模型不足可以处理的，要么通过纠正模型的结果，或者通过调整模型的参数不确定性生成预测不确定性的代表性，以某种方式来定义，模型不足的错误。后者的主要优点（以下简称 PUI，参数不确定性膨胀）是它的可转移性到预测其他感兴趣的数量基于相同的参数。关键审查的执行情况 PUI 在几个领域的计算化学表明，它是有偏见的，从某种意义上说，它不产生预测不确定性波段符合模型不足的错误。
