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2. Best-fit bin packing - Wikipedia

   en.wikipedia.org/wiki/Best-fit_bin_packing

   Best-fit is an online algorithm for bin packing. Its input is a list of items of different sizes. Its output is a packing - a partition of the items into bins of fixed capacity, such that the sum of sizes of items in each bin is at most the capacity. Ideally, we would like to use as few bins as possible, but minimizing the number of bins is an ...

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4. 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.

5. Histogram - Wikipedia

   en.wikipedia.org/wiki/Histogram

   Sturges's formula implicitly bases bin sizes on the range of the data, and can perform poorly if n < 30, because the number of bins will be small—less than seven—and unlikely to show trends in the data well. On the other extreme, Sturges's formula may overestimate bin width for very large datasets, resulting in oversmoothed histograms. [14]

6. High-multiplicity bin packing - Wikipedia

   en.wikipedia.org/wiki/High-multiplicity_bin_packing

   High-multiplicity bin packing is a special case of the bin packing problem, in which the number of different item-sizes is small, while the number of items with each size is large. While the general bin-packing problem is NP-hard , the high-multiplicity setting can be solved in polynomial time, assuming that the number of different sizes is a ...

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9. 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.