数据分析方法：选择直方图的分箱
Data analysis recipes: Choosing the binning for a histogram
摘要：
当创建直方图时，数据点被放置在箱中，但是总是要决定箱的数量或宽度。这种决定往往是武断或主观的，但也不一定如此。一个折刀或留一出交叉验证可能性被定义和采用作为一个标量目标函数优化的位置和宽度的箱。这个目标是合理的，因为它与直方图预测未来数据的有用性有关。该方法适用于任何维度的数据或直方图。
英文摘要：
Data points are placed in bins when a histogram is created, but there is always a decision to be made about the number or width of the bins. This decision is often made arbitrarily or subjectively, but it need not be. A jackknife or leave-one-out cross-validation likelihood is defined and employed as a scalar objective function for optimization of the locations and widths of the bins. The objective is justified as being related to the histogram's usefulness for predicting future data. The method works for data or histograms of any dimensionality.
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。
