Hypergeometric Distribution edit

Probability mass function
Cumulative distribution function
CDF   where   is the generalized hypergeometric function
Median mode =  
Ex. kurtosis  


Entropy ???

The hypergeometric distribution describes the number of successes in a sequence of n draws without replacement from a population of N that contained m total successes.

Its probability mass function is:


Technically the support for the function is only where x∈[max(0, n+m-N), min(m, n)]. In situations where this range is not [0,n], f(x)=0 since for k>0,  .

Probability Density Function edit

We first check to see that f(x) is a valid pmf. This requires that it is non-negative everywhere and that its total sum is equal to 1. The first condition is obvious. For the second condition we will start with Vandermonde's identity


We now see that if a=m and b=N-m that the condition is satisfied.

Mean edit

We derive the mean as follows:


We use the identity   in the denominator.


Next we use the identity   in the first binomial of the numerator.


Next, for the variables inside the sum we define corresponding prime variables that are one less. So N′=N−1, m′=m−1, x′=x−1, n′=n-1.


Now we see that the sum is the total sum over a Hypergeometric pmf with modified parameters. This is equal to 1. Therefore


Variance edit

We first determine E(X2).


We use the same variable substitution as when deriving the mean.


The first sum is the expected value of a hypergeometric random variable with parameteres (n',m',N'). The second sum is the total sum that random variable's pmf.


We then solve for the variance


or, equivalently,