论文标题
通过贝叶斯非参数估计相互信息的独立测试
A Test for Independence Via Bayesian Nonparametric Estimation of Mutual Information
论文作者
论文摘要
相互信息是衡量变量之间相互依赖性的知名工具。在本文中，通过Dirichlet过程和$ K $ - 最近的邻居距离建立了贝叶斯非参数估计。作为估计的直接结果，通过相对信仰比率引入了易于实现的独立性测试。提出了该方法的几种理论特性。通过将结果与其常见主义者对应的各种示例进行了研究，并证明了良好的性能。
Mutual information is a well-known tool to measure the mutual dependence between variables. In this paper, a Bayesian nonparametric estimation of mutual information is established by means of the Dirichlet process and the $k$-nearest neighbor distance. As a direct outcome of the estimation, an easy-to-implement test of independence is introduced through the relative belief ratio. Several theoretical properties of the approach are presented. The procedure is investigated through various examples where the results are compared to its frequentist counterpart and demonstrate a good performance.
