Return the optimal histogram bin width using Scott’s rule
Scott’s rule is a normal reference rule: it minimizes the integrated mean squared error in the bin approximation under the assumption that the data is approximately Gaussian.
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, freedman_bin_width, bayesian_blocks, histogram
Notes
The optimal bin width is
\Delta_b = \frac{3.5\sigma}{n^{1/3}}
where \sigma is the standard deviation of the data, and n is the number of data points [R19].
References
[R19] | (1, 2) Scott, David W. (1979). “On optimal and data-based histograms”. Biometricka 66 (3): 605-610 |