Aggregating disparate judgments using a coherence penalty

机译：使用相干罚则汇总不同的判断

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In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation are also discussed. Then a coherence penalty is defined and the Coherence Penalty Weighted Principle (CPWP) is proposed to take advantage of the data structure alongside the coherence approximation. Justification is given for the guideline that more coherent judges should be given larger weights. Simulation results with Brier Scores on both a collected database and simulated data are given for comparison. In addition to the CPWP, a recursive online variant with weight updates is presented to accommodate real-time aggregation problems.

机译：给出了解决概率判断集合问题的实用算法。首先，解释了Predd等人提出的可伸缩相干近似原理（CAP）算法，以及通过连续正交投影获得的计算节省。还讨论了德芬内蒂定理在这种情况下的含意。然后定义了一个相干罚分，并提出了相干罚分加权原理（CPWP）来利用数据结构以及相干近似。指南的理由是，应该给更多连贯的法官更大的权重。给出了在收集的数据库和模拟数据上均具有Brier分数的模拟结果，以进行比较。除了CPWP，还提出了具有权重更新的递归在线变量，以适应实时聚合问题。

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来源
《Information Sciences and Systems, 2009. CISS 2009》|2009年|23-27|共5页
会议地点 Baltimore MD(US);Baltimore MD(US);Baltimore MD(US)
作者
Guanchun Wang; Kulkarni S.; Poor H.V.;
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Dept. of Electr. Eng., Princeton Univ., Princeton, NJ;

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aggregation; approximation theory; coherence; probability; coherence penalty weighted principle; coherent approximation principle algorithm; data structure; disparate judgments; probabilistic judgment aggregation; successive orthogonal projection;

机译：聚合;近似理论;相干性;概率;相干罚加权原理;相干近似原理算法;数据结构;相异判断;概率判断聚集;连续正交投影;

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Aggregating disparate judgments using a coherence penalty

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