计算化学中校准预测不确定性的漫长道路
The long road to calibrated prediction uncertainty in computational chemistry
摘要：
计算化学中的不确定性量化（UQ）尚处于起步阶段。很少有CC方法被设计为提供其预测的置信水平，并且大多数用户仍然不正确地依赖于平均绝对误差作为准确度度量。可靠的不确定性量化方法的发展是必不可少的，特别是在工业过程中自信地使用计算化学。对CC-UQ文献的回顾表明，没有通用的标准程序来报告或验证预测不确定性。我在这里考虑使用气象学和机器学习中开发的概念（校准和锐度）来验证概率预报员的分析工具。这些工具适用于CC-UQ，并应用于复合方法，贝叶斯集成方法，机器学习和后验统计方法提供的预测不确定性数据集。
英文摘要：
Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable uncertainty quantification methods is essential, notably for computational chemistry to be used confidently in industrial processes. A review of the CC-UQ literature shows that there is no common standard procedure to report nor validate prediction uncertainty. I consider here analysis tools using concepts (calibration and sharpness) developed in meteorology and machine learning for the validation of probabilistic forecasters. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian Ensembles methods, machine learning and a posteriori statistical methods.
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