This short note is a critical discussion of the quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, which has recently been proposed in machine learning and has become quite common since then. More generally, we question the idea of an additive decomposition of total uncertainty into its aleatoric and epistemic constituents.

翻译：本简短说明是分别就有条件的催化酶和相互信息对显性不确定因素进行量化的批判性讨论,这最近在机器学习中提出,此后变得相当普遍。 更笼统地说,我们质疑将完全不确定因素添加进其显性成份和显性成份中的想法。