This creates a uniform distribution between a and b.

This creates a triangular distribution, with minimum a, mode b, and maximum c.

This creates a conditional probability table, assigning probabilities to outcomes, conditioned on outcomes of the inputs.

This converts an array y representing probability density values of x across index i into a continuous probability distribution.

This creates a normal distribution with mean m, and standard deviation s.

This creates a lognormal distribution with median m, and geometric standard deviation s.

This is used to specify an arbitrary continuous distribution by a list of n+1 elements si for i from 0 to n where si specifies the i/n fractile (quantile) of the distribution. Probability density is assumed uniform between fractiles.

This converts an array y representing cumulative probability values of x along index i into a continuous probability distribution.

This creates a discrete probability distribution, assigning an array of probabilities a to an array of outcomes u, across index i.

This creates a discrete probability distribution of probability p as follows: If Uniform( 0, 1 ) < p Then 1 Else 0.

This creates a distribution of numbers between 0 and 1 with x/(x+y) representing the mean.  For bounds other than 0 and 1, specify optional lower and upper bound (to offset and expand the distribution).