To learn more about EpiX Analytics' work, please visit our modeling applications, white papers, and training schedule.

Page tree

Versions Compared


  • This line was added.
  • This line was removed.
  • Formatting was changed.


The Cumulative distribution in Crystal Ball can be constructed using Crystal Ball's Custom distribution, and requires two arrays of data, {xi} and {Pi}) where {xi} is an array of x-values with cumulative probabilities {Pi} and where the distribution falls between the minimum and maximum. The figure below shows the Cumulative distribution using data ({0,1,4,6,10},{0,0.1,0.6,0.8,1.0}) as it is defined in its cumulative form and how it looks as a relative frequency plot.





1. Empirical distribution of data

The Cumulative distribution is very useful for converting a set of data values into a first or second order empirical distribution that can be sampled by Crystal Ball.


2. Modeling expert opinion

The Cumulative distribution can be used to construct uncertainty distributions when using some classical statistical methods. Examples: p in a Binomial process; l in a Poisson process.


3. Modeling expert opinion

The Cumulative distribution is used in some texts to model expert opinion. The expert is asked for a minimum, maximum and a few percentiles (e.g. 25%, 50%, 75%). However, we have found it largely unsatisfactory because of the insensitivity of its probability scale. A small change in the shape of the Cumulative distribution that would pass unnoticed produces a radical change in the corresponding relative frequency plot that would not be acceptable. The figure below provides an illustration: