Blurb::
Aleatory uncertain discrete variable - Poisson

Description::
The Poisson distribution is used to predict the number of discrete events that happen in a single time interval.  The random events occur uniformly and independently.
The expected number of occurences in a single time interval is \f$\lambda\f$, which must be a positive real number.  For example, if events occur on average 4 times per year and we are interested in the distribution of events over six months, \f$\lambda\f$ would be 2.  However, if we were interested in the distribution of events occuring over 5 years, \f$\lambda\f$ would be 20.
 
The probability mass function for the poisson distribution is given by:
\f[f(x) = \frac{\lambda^{x} e^{-\lambda}}{x!}\f]
where 
-\f$\lambda\f$ is the expected number of events occuring in a single time interval
-\c x is the number of events that occur in this time period 
-f(x) is the probability that \c x events occur in this time period


Topics:: 	discrete_variables, aleatory_uncertain_variables
Examples::
Theory::

When used with some methods such as design of experiments and
multidimensional parameter studies, distribution bounds are inferred
to be [0, \f$\mu + 3 \sigma\f$].

For some methods, including vector and centered parameter studies, an
initial point is needed for the uncertain variables. When not given
explicitly, these variables are initialized to their means.

Faq::
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