论文标题
基于Wasserstein信息矩阵的先验
On a prior based on the Wasserstein information matrix
论文作者
论文摘要
我们基于Wasserstein信息矩阵，为单变量连续分布的参数介绍了一个先验，该矩阵在重新聚集下是不变的。我们讨论了与信息几何形状之间提出的先验之间的联系。我们为通用模型的后验分布的适当性提供了足够的条件。我们提出了一项模拟研究，该研究表明诱导的后期具有良好的频繁特性。
We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discuss the links between the proposed prior with information geometry. We present sufficient conditions for the propriety of the posterior distribution for general classes of models. We present a simulation study that shows that the induced posteriors have good frequentist properties.
