The __Discrete distribution__ in Crystal Ball can be constructed using Crystal Ball's Custom distribution, and requires two arrays of data, *{x** _{i}} and {p_{i}}*) where

*{*

*x*

_{i}

*}*is an array of the possible values of the variable with the probability weightings

*{*

*p*

_{i}*} in the second array*. The

*{*

*p*

_{i}*}*values do not have to add up to unity as Crystal Ball software will normalize them automatically. It is actually often useful just to consider the ratio of likelihood of the different values and not to worry about the actual probability values. The discrete distribution can be used to model a discrete parameter (that is, a parameter that may take one of two or more distinct values), e.g. the number of turbines that will be used in a power station, and to combine two or more conflicting expert opinions (see section on Incorporating differences in expert opinions).