标题： 概率校准预报 显示英文标题

标题： Probabilistic Recalibration of Forecasts

摘要： 我们提出了一种方案，通过该方案，可以将一个预测结果概率校准较差的概率预报系统，通过结合过去的表现信息，生成一个新的预报系统，该系统在表现上明显优于原始系统，因为可以用它来持续地在赌注中战胜使用原始系统的对手。 该方案利用高斯过程（GP）建模来估计标量预测变量的概率积分变换（PIT）的概率分布。 GP密度估计提供了对与估计分布相关的信息熵度量的显式访问，这允许预测与基础预报系统进行赌注时的收益。 该过程的一个附加结果是，重新校准的预报具有均匀的期望PIT分布。 该过程的一个显著特点是，即使PIT值不是独立同分布的，该方法也是适用的。 重新校准方案是在一个利用信息论、预报和赌博之间深刻联系的框架中制定的。 我们在两个案例研究中展示了该方案的有效性：一个非线性电路的实验室实验和厄尔尼诺-南方涛动现象强度的季节性预报。 显示英文摘要

摘要： We present a scheme by which a probabilistic forecasting system whose predictions have poor probabilistic calibration may be recalibrated by incorporating past performance information to produce a new forecasting system that is demonstrably superior to the original, in that one may use it to consistently win wagers against someone using the original system. The scheme utilizes Gaussian process (GP) modeling to estimate a probability distribution over the Probability Integral Transform (PIT) of a scalar predictand. The GP density estimate gives closed-form access to information entropy measures associated with the estimated distribution, which allows prediction of winnings in wagers against the base forecasting system. A separate consequence of the procedure is that the recalibrated forecast has a uniform expected PIT distribution. A distinguishing feature of the procedure is that it is appropriate even if the PIT values are not i.i.d. The recalibration scheme is formulated in a framework that exploits the deep connections between information theory, forecasting, and betting. We demonstrate the effectiveness of the scheme in two case studies: a laboratory experiment with a nonlinear circuit and seasonal forecasts of the intensity of the El Ni\~no-Southern Oscillation phenomenon.