Data analysis recipes: Choosing the binning for a histogram

Understand

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.