标题： 基于Copula的依存结构估计 显示英文标题

标题： Dependence Structure Estimation via Copula

摘要： 依赖结构估计是机器学习领域的一个重要问题，并在不同的科学领域中有许多应用。 在本文中，提出了一种基于Copula和Copula熵的理论框架——概率表示和测量统计依赖性的理论。 图形模型被视为Copula框架的一个特例。 提出了一种该框架用于估计最大生成Copula的方法。 由于Copula，该方法与单个变量的性质无关，对异常值不敏感，并能够处理非高斯性。 在模拟数据和真实数据集上的实验证明了所提出方法的有效性。 显示英文摘要

摘要： Dependence strucuture estimation is one of the important problems in machine learning domain and has many applications in different scientific areas. In this paper, a theoretical framework for such estimation based on copula and copula entropy -- the probabilistic theory of representation and measurement of statistical dependence, is proposed. Graphical models are considered as a special case of the copula framework. A method of the framework for estimating maximum spanning copula is proposed. Due to copula, the method is irrelevant to the properties of individual variables, insensitive to outlier and able to deal with non-Gaussianity. Experiments on both simulated data and real dataset demonstrated the effectiveness of the proposed method.