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Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

机译：用贝叶斯信号检测模型评估概率预测

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We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.
机译：我们建议使用信号检测理论（SDT）来评估概率预测系统和单个预测器的性能。 SDT的主要优点在于，它提供了一种区分响应和系统诊断性的原则方法，系统诊断性是指将发生的事件与未发生的事件区分开的能力。将SDT应用于概率预测存在两个挑战。首先，SDT模型必须处理判断的概率，而不是常规的二进制决策。其次，该模型必须能够在人类预测系统的环境中生成的稀疏数据存在下运行。我们的方法是指定一个模型，该模型说明如何从基础表示形式生成单个预测，并使用贝叶斯推断来估算基础潜在参数。给定我们对基本表示形式的估计，可以立即遵循经典SDT模型的功能，例如接收器工作特性（ROC）曲线和ROC曲线下方的面积（AUC）。我们展示了我们的方法如何允许将ROC曲线和AUC应用于一组预测器中的个人，并作为时间的函数进行估计，并扩展到衡量不同领域的可预测性差异。这种方法的优点之一是它仅取决于概率预测的序数属性。最后，我们简要讨论此方法如何促进决策。

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