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The histogram is an analysis tool in widespread use within many sciences,with high energy physics as a prime example. However, there exists an inherentbias in the choice of binning for the histogram, with different choicespotentially leading to different interpretations. This paper aims to eliminatethis bias using two "debinning" algorithms. Both algorithms generate anobserved cumulative distribution function from the data, and use it toconstruct a representation of the underlying probability distribution function.The strengths and weaknesses of these two algorithms are compared andcontrasted. The applicability and future prospects of these algorithms is alsodiscussed.

Resolving Histogram Binning Dilemmas with Binless and Binfull Algorithms