optBINS: Optimal Binning for histograms
Abstract
optBINS (optimal binning) determines the optimal number of bins in a uniform binwidth histogram by deriving the posterior probability for the number of bins in a piecewiseconstant density model after assigning a multinomial likelihood and a noninformative 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.
 Publication:

Astrophysics Source Code Library
 Pub Date:
 March 2018
 Bibcode:
 2018ascl.soft03013K
 Keywords:

 Software