Return the optimal histogram bin width using the Freedman-Diaconis rule
The Freedman-Diaconis rule is a normal reference rule like Scott’s rule, but uses rank-based statistics for results which are more robust to deviations from a normal distribution.
Parameters: | data : array-like, ndim=1
return_bins : bool (optional)
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Returns: | width : float
bins : ndarray
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See also
knuth_bin_width, scott_bin_width, bayesian_blocks, histogram
Notes
The optimal bin width is
\Delta_b = \frac{2(q_{75} - q_{25})}{n^{1/3}}
where q_{N} is the N percent quartile of the data, and n is the number of data points [R17].
References
[R17] | (1, 2) D. Freedman & P. Diaconis (1981) “On the histogram as a density estimator: L2 theory”. Probability Theory and Related Fields 57 (4): 453-476 |