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Inference assessment on a probability scale

机译:概率量表的推理评估

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The performance of a probability inference or forecast can be expressed as the average forecasted probability by computing the geometric mean of the forecasted probabilities. This metric is obtained by transforming the information theoretic measure, commonly known as the logarithmic score or the average surprisal, into the probability domain via application of the exponential function. The average probability forecast is composed of two sources of uncertainty, the underlying uncertainty of the data source and the divergence between the model and source. Expressed as probabilities, these three measures of the performance can be plotted as part of the probability calibration curve comparing the forecasted probabilities with the measured source probability. Insight into the under- or over-confidence of the model is obtained by computing the weighted generalized mean of the forecasted and source probabilities. A comparison of the 2016 US Presidential election forecasts is used to illustrate the utility of the method.
机译:通过计算预测概率的几何平均值,可以将概率推断或预测的性能表示为平均预测概率。通过使用指数函数将信息理论量度(通常称为对数得分或平均意外数)转换为概率域,可以获取此度量。平均概率预测由两个不确定性源组成,数据源的潜在不确定性和模型与源之间的差异。以概率表示,这三种性能度量可以作为概率校准曲线的一部分进行绘制,将预测的概率与测得的源概率进行比较。通过计算预测概率和源概率的加权广义均值,可以洞悉模型的置信度过低或过高。通过对2016年美国总统大选的预测进行比较,可以说明该方法的实用性。

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