  The histogram is an analysis tool in widespread use within many sciences,
with high energy physics as a prime example. However, there exists an inherent
bias in the choice of binning for the histogram, with different choices
potentially leading to different interpretations. This paper aims to eliminate
this bias using two "debinning" algorithms. Both algorithms generate an
observed cumulative distribution function from the data, and use it to
construct a representation of the underlying probability distribution function.
The strengths and weaknesses of these two algorithms are compared and
contrasted. The applicability and future prospects of these algorithms is also
discussed.
