Good-Turing估计的均方精度
Mean-Squared Accuracy of Good-Turing Estimator
University of Luxembourg（卢森堡大学）
摘要：
Good和Turing的杰出方法允许估计样本中没有出现的对象。这个问题被称为“样本覆盖率”或“缺失质量”，可以追溯到他们在二战期间的加密工作，但多年来已经发现了许多应用，包括语言建模，生态学推断和分布特性估计。这项工作的特点是最大的均方误差的好图灵估计，任何样本\emph{and}字母大小。
英文摘要：
The brilliant method due to Good and Turing allows for estimating objects not occurring in a sample. The problem, known under names "sample coverage" or "missing mass" goes back to their cryptographic work during WWII, but over years has found has many applications, including language modeling, inference in ecology and estimation of distribution properties. This work characterizes the maximal mean-squared error of the Good-Turing estimator, for any sample \emph{and} alphabet size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
