温度的概率预报：对贝叶斯模型平均法的评论
Probabilistic forecasting of temperature: comments on the Bayesian Model Averaging approach
摘要：
最近提出了一个具体的实施贝叶斯模型平均作为一种方法，集合温度预报的校准。我们指出这种新方法和早期的方法称为内核回归之间的相似之处。我们还认为，贝叶斯模型的平均方法（如应用）有一些缺陷，将导致次优校准的平均值和不确定性的预测。
英文摘要：
A specific implementation of Bayesian model averaging has recently been suggested as a method for the calibration of ensemble temperature forecasts. We point out the similarities between this new approach and an earlier method known as kernel regression. We also argue that the Bayesian model averaging method (as applied) has a number of flaws that would result in forecasts with suboptimally calibrated mean and uncertainty.
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