The Shannon Entropy of a Histogram
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Oct 2022.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
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- 2023Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?18 Oct 2023