论文标题
副群体密度神经估计
Copula Density Neural Estimation
论文作者
论文摘要
观察到的数据的概率密度估计构成统计中的核心任务。在此简介中，我们重点介绍了估计与任何观察到的数据相关的副库密度的问题，因为它充分描述了随机变量之间的依赖性。我们将单变量的边际分布与数据，Copula本身中的关节依赖性结构分开，并用基于神经网络的方法对后者进行建模，称为Copula密度神经估计（Codine）。结果表明，新型学习方法能够对复杂的分布进行建模，并可以应用于相互信息估计和数据生成。
Probability density estimation from observed data constitutes a central task in statistics. In this brief, we focus on the problem of estimating the copula density associated to any observed data, as it fully describes the dependence between random variables. We separate univariate marginal distributions from the joint dependence structure in the data, the copula itself, and we model the latter with a neural network-based method referred to as copula density neural estimation (CODINE). Results show that the novel learning approach is capable of modeling complex distributions and can be applied for mutual information estimation and data generation.
