Blurb::
Aleatory uncertain discrete variable - binomial
Description::
The binomial distribution describes probabilities associated with a series
of independent Bernoulli trials.
A Bernoulli trial is an event with two mutually exclusive outcomes,
such as 0 or 1, yes or no, success or fail.
The probability of success remains the same (the trials are independent).

The density function for the binomial distribution is given by: 
\f[f(x) = \left(\begin{array}{c}n\\x\end{array}\right){p^x}{(1-p)^{(n-x)}}\f]
where \c p is the 
probability of failure per trial,  \c n is the number of trials and
\c x is the number of successes.

Topics::	discrete_variables, aleatory_uncertain_variables
Examples::
Theory::
The binomial distribution is typically used to predict the number of failures
or defective items in a total of \c n independent tests or trials,
where each trial has the probability \c p of failing or being defective. 
 
Faq::
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