论文标题
基于混合的熵估计
Mixture-based estimation of entropy
论文作者
论文摘要
熵是在信息理论中起着核心作用的不确定性的度量。当数据的分布未知时，需要从数据样本本身获得熵的估计。我们基于感兴趣分布的混合模型近似，提出了半参数估计。估计值可以依赖于任何类型的混合物，但是我们专注于高斯混合模型以证明其准确性和多功能性。通过一系列模拟研究评估了所提出的方法的性能。我们还说明了它在两个现实生活中的数据示例中的用途。
The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs be obtained from the data sample itself. We propose a semi-parametric estimate, based on a mixture model approximation of the distribution of interest. The estimate can rely on any type of mixture, but we focus on Gaussian mixture model to demonstrate its accuracy and versatility. Performance of the proposed approach is assessed through a series of simulation studies. We also illustrate its use on two real-life data examples.
