Sahlin Ullrika
Centre of Environmental and Climate Research, Lund University, Lund, Sweden,
J Comput Aided Mol Des. 2015 Jul;29(7):583-94. doi: 10.1007/s10822-014-9822-3. Epub 2014 Dec 10.
A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it.
对化学性质或活性的预测存在不确定性。考虑哪些类型的不确定性、是否以差异化方式对其进行考量以及采用何种方法，取决于实际情况。在化学建模中，由于基础建模算法种类繁多、存在高维问题、需要兼顾不确定性的定性和定量维度，以及统计学提供了不确定性量化的替代原则，因此对不确定性评估的一般指导受到阻碍。在此，提出一种预测中不确定性评估的观点，旨在克服这些问题。该评估旨在量化表示预测误差的不确定性，并且基于误差的概率建模，其中不确定性通过贝叶斯概率来衡量。尽管动机充分，但使用贝叶斯概率的选择对统计学和化学建模而言是一项挑战。文中讨论了全贝叶斯建模、贝叶斯元建模和自举法等可能的方法。决定如何评估不确定性是一种主动选择，不应受传统观念或缺乏经过验证的可靠方法的限制。
