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optBINS: Optimal Binning for histograms

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Ada Coda
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optBINS: Optimal Binning for histograms

Postby Ada Coda » Sun Apr 01, 2018 3:21 am

optBINS: Optimal Binning for histograms

Abstract: optBINS (optimal binning) determines the optimal number of bins in a uniform bin-width histogram by deriving the posterior probability for the number of bins in a piecewise-constant density model after assigning a multinomial likelihood and a non-informative prior. The maximum of the posterior probability occurs at a point where the prior probability and the the joint likelihood are balanced. The interplay between these opposing factors effectively implements Occam's razor by selecting the most simple model that best describes the data.

Credit: Knuth, Kevin H.


Bibcode: 2018ascl.soft03013K

Preferred citation method:

ID: ascl:1803.013

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