论文标题
kl差异的最佳估计用于连续分布
Minimax Optimal Estimation of KL Divergence for Continuous Distributions
论文作者
论文摘要
在各个领域中，估计与相同和独立分布样本的kullback-leibler差异是一个重要的问题。一个简单有效的估计器基于这些样品之间的K最近邻居距离。在本文中，我们分析了该估计器的偏差和方差的收敛速率。此外，我们得出了Minimax均方根误差的下限，并表明KNN方法在渐近上是最佳速率。
Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the k nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal.
