论文标题
实证贝叶斯方法的真相发现问题
Empirical Bayes approach to Truth Discovery problems
论文作者
论文摘要
当从冲突来源汇总信息时，一个人的目标是找到真相。大多数真实价值\ emph {truth discovery}（TD）算法试图通过估计每个源的能力，然后通过按比例地权衡每个源的答案与她的能力来实现这一目标。但是，这些算法中的每一种都需要此类估计的单个来源，通常不考虑除加权平均值以外的其他估计方法。因此，在这项工作中，我们制定，证明和经验测试了经验贝叶斯估计量（EBE）的条件，以主导加权平均聚集。我们的主要结果表明，在轻度条件下，EBE可以用作任何TD算法的第二步，以减少预期误差。
When aggregating information from conflicting sources, one's goal is to find the truth. Most real-value \emph{truth discovery} (TD) algorithms try to achieve this goal by estimating the competence of each source and then aggregating the conflicting information by weighing each source's answer proportionally to her competence. However, each of those algorithms requires more than a single source for such estimation and usually does not consider different estimation methods other than a weighted mean. Therefore, in this work we formulate, prove, and empirically test the conditions for an Empirical Bayes Estimator (EBE) to dominate the weighted mean aggregation. Our main result demonstrates that EBE, under mild conditions, can be used as a second step of any TD algorithm in order to reduce the expected error.
