The table below gives an overview of the various discrete distributions = described in ModelAssist, so that you can most easily focus on which ones m= ight be most appropriate for your modeling needs. Follow the links for an i= ndepth explanation of each. We have used the most common name for each dis= tribution.
Distributions 
Example use 
Returns a 1 with probability p and a zero oth= erwise. 

Shows the number of successes from n independ= ent trials where there is a probability p of success in each trial.  
A binomial variable where p is also a Betadi= stributed random variable. 

Describes a variable that can take one of sev= eral explicit discrete values with different probabilities. 

Describes a variable that can take one of sev= eral explicit discrete values with equal probabilities. 

Models the total number of trials that will o= ccur before a success, given that p is the probability of succeeding.  
Models the number of items of a particular ty= pe there will be in a sample of size n where that sample is drawn from a po= pulation of size M of which D are also of that particular type. 

Describe a variable that can take one of seve= ral sequential discrete values. 

Models the total number of trials one would h= ave to do before achieving the sth success in a hypergeometric sampling. 

A one parameter, positive distribution someti= mes used to model frequency of insurance claims. Also used for insect speci= es abundance 

An extension of the Binomial distribution whe= re more than two different states of a trial exist. 

An extension of the Hypergeometric distributi= on where more than two subpopulations of interest exist. 

Models the total number of trials there will = be before s successes are achieved where there is a probability p of succes= s with each trial. Also models a Poisson random variable whose mean is a (G= amma) random variable. 

Models the number of occurrences of an event = in a time t when the time between successive events follows a Poisson proce= ss 
A discrete distribution may take one of a set of identifiable values, ea= ch of which has a calculable probability of occurrence. Discrete distributi= ons are used to model parameters like the number of bridges a roading schem= e may need, the number of key personnel to be employed or the number of cus= tomers that will arrive at a service station in an hour. Clearly, variables= such as these can only take specific values: one cannot build half a bridg= e, employ 2.7 people or serve 13.6 customers.
The vertical scale of a relative frequency plot of a discrete distributi= on is the actual probability of occurrence, sometimes called the p= robability mass. These probabilities must sum to one.
The most common examples of discrete distributions are: Binomial, = Geometric, Hypergeometric, Negative Binomial,&n= bsp;Poisson and, of course, the ge= neralized Discrete distribut= ion. The links below discuss different ways of categorizing distributions t= hat may help in your selection of the most appropriate distribution to use:=
Bounded and = unbounded distributions
Para= metric and nonparametric distributions