大型字母表分布建模算法

A. Orlitsky, Sajama, N. Santhanam, K. Viswanathan, Junan Zhang
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引用次数: 21

摘要

我们考虑的问题是,如何对字母表大小相对于观测数据量较大的分布进行建模。众所周知,传统的最大似然估计在这种情况下表现不佳。相反,我们需要找到一个最大化数据模式概率的分布。我们推导出一种近似这种分布的高效算法。仿真表明,计算出的分布能很好地模拟数据,并产生能评估各种数据属性的通用估计器,以及专为这些任务设计的特定估计器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithms for modeling distributions over large alphabets
We consider the problem of modeling a distribution whose alphabet size is large relative to the amount of observed data. It is well known that conventional maximum-likelihood estimates do not perform well in that regime. Instead, we find the distribution maximizing the probability of the data's pattern. We derive an efficient algorithm for approximating this distribution. Simulations show that the computed distribution models the data well and yields general estimators that evaluate various data attributes as well as specific estimators designed especially for these tasks
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