论文标题
直方图的香农熵
The Shannon Entropy of a Histogram
论文作者
论文摘要
直方图是可视化数据和估计潜在概率分布的关键方法。关于数据产生的数据的不正确结论。基于直方图的香农熵的新方法使用基于从最近的邻居距离估计的差分熵的简单公式。新方法与其他算法（例如Scott的公式）以及成本和风险功能方法之间的链接。发现一个预测上层和下层的参数，可以估计任何直方图。通过应用于真实数据，新算法被证明是可靠的。
The histogram is a key method for visualizing data and estimating the underlying probability distribution. Incorrect conclusions about the data result from over or under-binning. A new method based on the Shannon entropy of the histogram uses a simple formula based on the differential entropy estimated from nearest-neighbour distances. Links are made between the new method and other algorithms such as Scott's formula, and cost and risk function methods. A parameter is found that predicts over and under-binning, which can be estimated for any histogram. The new algorithm is shown to be robust by application to real data.
