  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.
