From data to probability densities without histograms

2019-12-06T07:34:06Z (GMT) by Bernd A. Berg Robert C. Harris
Abstract When one deals with data drawn from continuous variables, a histogram is often inadequate to display their probability density. It deals inefficiently with statistical noise, and binsizes are free parameters. In contrast to that, the empirical cumulative distribution function (obtained after sorting the data) is parameter free. But it is a step function, so that its differentiation does not give a smooth probability density. Based on Fourier series expansion and Kolmogorov tests, we introduce... Title of program: cdf_to_pd Catalogue Id: AEBC_v1_0 Nature of problem When one deals with data drawn from continuous variables, a histogram is often inadequate to display the probability density. It deals inefficiently with statistical noise, and binsizes are free parameters. In contrast to that, the empirical cumulative distribution function (obtained after sorting the data) is parameter free. But it is a step function, so that its differentiation does not give a smooth probability density. Versions of this program held in the CPC repository in Mendeley Data AEBC_v1_0; cdf_to_pd; 10.1016/j.cpc.2008.03.010 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)