Blurb::
Aleatory uncertain discrete variable - hypergeometric
Description::
The hypergeometric probability density is used when sampling without replacement from a total population
of elements where
- The resulting element of each sample can be separated into one of two non-overlapping sets
- The probability of success changes with each sample.

The density function for the hypergeometric distribution is given by: 
\f[f(x) = \frac{\left(\begin{array}{c}m\\x\end{array}\right)\left(\begin{array}{c}{N-m}\\{n-x}\end{array}\right)}{\left(\begin{array}{c}N\\n\end{array}\right)}\f] 
where the three distribution parameters are:
- N is the total population
- m is the number of items in the selected population (e.g. the number of white balls in the full urn of N items)
- n is the size of the sample drawn (e.g. number of balls drawn)

In addition,
- x, the abscissa of the density function, indicates the number of successes (e.g. drawing a white ball)
- \f$\left(\begin{array}{c}a\\b\end{array}\right)\f$ indicates a binomial coefficient ("a choose b")

Topics::	discrete_variables, aleatory_uncertain_variables
Examples::
Theory::
The hypergeometric is often described using an urn model. For example, say we have a total population containing N balls, and we know that m of the balls are white and the remaining balls are green. If we draw n balls from the urn without replacement, the hypergeometric distribution describes the probability of drawing x white balls.
Faq::
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