Bayesian Correlation Score: A Utilitarian Measure of Forecast Skill
Bayesian Correlation Score: A Utilitarian Measure of Forecast Skill
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贝叶斯相关分数：预测技能的实用衡量标准
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发表时间：
1992
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通讯作者：
Roman Krzysztofowicz
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作者：
Roman Krzysztofowicz
本文从实验充分比较的理论出发，导出了连续预测量的分类预测的技巧测度。贝叶斯相关性（BCS）是一个正态线性统计模型的三个参数，该模型结合了两个来源的信息：预测对象的先验（气候学）记录和预测的验证记录。BCS有三个特征：（1）它对比较同一预测对象的备选预测和不同预测对象的预测都有意义，但意义有限：（2）它可以解释为预测对象和预测对象之间的相关性;最重要的是（iii）它向理性用户订购与其事前经济价值一致的替代预测系统（在预测变量的后验分布下，通过最大化结果的预期效用来做出决策的人）。因此，通过最大化BCS，预测者可以确保一个功利的，所以…
Abstract From the theory of sufficient comparisons of experiments, a measure of skill is derived for categorical forecasts of continuous predictands. Called Bayesian correlation wore (BCS), the measure is specified in terms of three parameters of a normal-linear statistical model that combines information from two sources: a prior (climatological) record of the predictand and a verification record of forecasts. Three properties characterize the BCS: (i) It is meaningful for comparing alternative forecasts of the same predictand, as well as forecasts of different predictands, though in a limited sense; (ii) it is interpretable as correlation between the forecast and the predictand; and, most significantly, (iii) it orders alternative forecast systems consistently with their ex ante economic values to rational users (those who make decisions by maximizing the expected utility of outcomes under the posterior distribution of the predictand). Thus, by maximizing the BCS, forecasters can assure a utilitarian so...