From data to probability densities without histograms
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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)
创建时间：
2008-09-15
