Abstract

The probability distributions that statistical methods use to represent uncertainty fail to capture all of the uncertainty that may be relevant to decision making. A simple way to adjust probability distributions for the uncertainty not represented in their models is to average the distributions with a uniform distribution or another distribution of maximum uncertainty. A decision-theoretic framework leads to averaging the distributions by taking the means of the logit transforms of the probabilities. That method does not prevent convergence to the truth, as does taking the means of the probabilities themselves. The mean-logit approach to moderating distributions is applied to natural language processing performed by a deep neural network.

摘要

统计方法用来表示不确定性的概率分布未能捕捉到可能与决策相关的所有不确定性。调整模型中未表示的不确定性的概率分布的一种简单方法是对具有均匀分布或最大不确定性的另一种分布的分布进行平均。决策理论框架通过采用概率的 logit 变换来平均分布。这种方法不会阻止对真理的收敛，就像采用概率本身的手段一样。调节分布的均值-logit 方法应用于由深度神经网络执行的自然语言处理。