Optimal probability aggregation based on generalized brier scoring
Optimal probability aggregation based on generalized brier scoring
复制标题
基于广义brier评分的最优概率聚合
DOI：
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发表时间：
2019
影响因子：
1.2
通讯作者：
G. Schurz
中科院分区：
文献类型：
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作者：
Christian J. Feldbacher;G. Schurz
在本文中，我们将概率汇总理论与机器学习理论的结果结合在一起，涉及专家建议下的预测的最佳性。在概率汇总理论中，存在线性聚集的几种表征结果。但是，在线性聚合中，权重不是固定，而是免费参数。我们展示了如何通过基于成功的分数来固定权重的限制，即Brier评分的概括，可以将上述最佳结果转移到概率聚集的情况下。
In this paper we combine the theory of probability aggregation with results of machine learning theory concerning the optimality of predictions under expert advice. In probability aggregation theory several characterization results for linear aggregation exist. However, in linear aggregation weights are not fixed, but free parameters. We show how fixing such weights by success-based scores, a generalization of Brier scoring, allows for transferring the mentioned optimality results to the case of probability aggregation.