论文标题
关于不确定性定量中模型错误指定的观点
A view on model misspecification in uncertainty quantification
论文作者
论文摘要
估计机器学习模型的不确定性对于评估这些模型提供的预测的质量至关重要。但是，有几个因素会影响不确定性估计的质量，其中之一是模型错误指定的量。模型错误指定始终存在，因为模型仅仅是对现实的简化或近似值。出现的问题是，模型错误指定下的估计不确定性是否可靠。在本文中，我们认为模型错误指定应通过提供思想实验并将其与相关文献相关化，从而受到更多关注。
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximations to reality. The question arises whether the estimated uncertainty under model misspecification is reliable or not. In this paper, we argue that model misspecification should receive more attention, by providing thought experiments and contextualizing these with relevant literature.
