2014/05/20 by Abram Krislock, Krislock, Abram, Nathan Krislock +1 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Algorithms and Data Compression #Artificial intelligence #Bayesian Methods and Mixture Models #Combinatorics #Computer science #Construct (python library) #Data Analysis #FOS: Physical sciences #Function (biology) #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Histogram #Image (mathematics) #Mathematics #Particle physics theoretical and experimental studies #Prime (order theory) #Representation (politics) #Statistics and Probability (physics.data-an) #Strengths and weaknesses #hep-ex #hep-ph #physics.data-an
paper · pdf · doi:10.48550/arxiv.1405.4958
published in arXiv (Cornell University) (Cornell University) · 19 pages, 5 figures; additional material to be found at https://debinning.hepforge.org/
arxiv created 2014/05/20 · openalex publication_date 2014/05/20 · arxiv updated 2014/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.