论文标题
计算化学方法的概率性能估计器：系统的改进概率和排名概率矩阵。 ii。申请
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. II. Applications
论文作者
论文摘要
在这项研究的第一部分（论文I）中，我们引入了系统的改进概率（SIP），作为评估两种计算化学方法之间切换时预期的绝对误差改进水平的工具。我们还基于强大的统计数据开发了两个指标，以解决计算化学基准中排名的不确定性：PINV，两个统计值之间的反转概率，而PR，排名概率矩阵。在第二部分中，这些指标应用于从最近的基准文献中提取的九个数据集。我们还说明了误差集之间的相关性如何包含基准数据集质量上的有用信息，特别是当使用实验数据作为参考时。
In the first part of this study (Paper I), we introduced the systematic improvement probability (SIP) as a tool to assess the level of improvement on absolute errors to be expected when switching between two computational chemistry methods. We developed also two indicators based on robust statistics to address the uncertainty of ranking in computational chemistry benchmarks: Pinv , the inversion probability between two values of a statistic, and Pr , the ranking probability matrix. In this second part, these indicators are applied to nine data sets extracted from the recent benchmarking literature. We illustrate also how the correlation between the error sets might contain useful information on the benchmark dataset quality, notably when experimental data are used as reference.
