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  2. Freedman–Diaconis rule - Wikipedia

    en.wikipedia.org/wiki/Freedman–Diaconis_rule

    10000 samples from a normal distribution data binned using different rules. The Freedman-Diaconis rule results in 61 bins, the Scott rule 48 and Sturges' rule 15. With the factor 2 replaced by approximately 2.59, the Freedman–Diaconis rule asymptotically matches Scott's Rule for data sampled from a normal distribution.

  3. Histogram - Wikipedia

    en.wikipedia.org/wiki/Histogram

    The number of bins k can be assigned directly or can be calculated from a suggested bin width h as: Histogram data represented with different bin widths k = ⌈ max x − min x h ⌉ . {\displaystyle k=\left\lceil {\frac {\max x-\min x}{h}}\right\rceil .}

  4. Scott's rule - Wikipedia

    en.wikipedia.org/wiki/Scott's_Rule

    Scott's rule is a method to select the number of bins in a histogram. [1] Scott's rule is widely employed in data analysis software including R, [2] Python [3] and Microsoft Excel where it is the default bin selection method. [4]

  5. Sturges's rule - Wikipedia

    en.wikipedia.org/wiki/Sturges's_rule

    Sturges's rule [1] is a method to choose the number of bins for a histogram. Given observations, Sturges's rule suggests using ^ = + ⁡ bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method. [3]

  6. Entropy estimation - Wikipedia

    en.wikipedia.org/wiki/Entropy_estimation

    with bin probabilities given by that histogram. The histogram is itself a maximum-likelihood (ML) estimate of the discretized frequency distribution [citation needed]), where is the width of the th bin. Histograms can be quick to calculate, and simple, so this approach has some attraction.

  7. V-optimal histograms - Wikipedia

    en.wikipedia.org/wiki/V-optimal_histograms

    A v-optimal histogram is based on the concept of minimizing a quantity which is called the weighted variance in this context. [1] This is defined as = =, where the histogram consists of J bins or buckets, n j is the number of items contained in the jth bin and where V j is the variance between the values associated with the items in the jth bin.

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  9. Bin (computational geometry) - Wikipedia

    en.wikipedia.org/wiki/Bin_(computational_geometry)

    The size of a candidate's array is the number of bins it intersects. For example, in the top figure, candidate B has 6 elements arranged in a 3 row by 2 column array because it intersects 6 bins in such an arrangement. Each bin contains the head of a singly linked list. If a candidate intersects a bin, it is chained to the bin's linked list.