论文标题
改进的香农熵估计器，并应用于具有内存的系统
An improved estimator of Shannon entropy with applications to systems with memory
论文作者
论文摘要
我们研究了基于有限数量状态的离散序列的内存属性。我们发现，块熵可以可靠地确定以任意有限顺序的马尔可夫链建模的系统的内存。此外，我们提供了一个熵估计器，当存在相关性时，它可以显着提供准确的结果。为了说明我们的发现，我们计算了不同位置的每日降水序列的记忆。我们的结果与现有方法同时在不足的采样制度中有效，并且独立于模型选择有效。
We investigate the memory properties of discrete sequences built upon a finite number of states. We find that the block entropy can reliably determine the memory for systems modeled as Markov chains of arbitrary finite order. Further, we provide an entropy estimator that remarkably gives accurate results when correlations are present. To illustrate our findings, we calculate the memory of daily precipitation series at different locations. Our results are in agreement with existing methods being at the same time valid in the undersampled regime and independent of model selection.
