The Poisson pdf is

$$f(x|\lambda )=\frac{{\lambda}^{x}}{x!}{e}^{-\lambda}\text{\hspace{0.17em}};\text{\hspace{0.17em}}x=0,1,2,\dots ,\infty \text{\hspace{0.17em}}.$$

The Poisson distribution is appropriate for applications that involve counting the number of times a random event occurs in a given amount of time, distance, area, etc. Sample applications that involve Poisson distributions include the number of Geiger counter clicks per second, the number of people walking into a store in an hour, and the number of flaws per 1000 feet of video tape.

The Poisson distribution is a one-parameter discrete distribution
that takes nonnegative integer values. The parameter, *λ*,
is both the mean and the variance of the distribution. Thus, as the
size of the numbers in a particular sample of Poisson random numbers
gets larger, so does the variability of the numbers.

The Poisson distribution is the limiting case of a binomial
distribution where *N* approaches infinity and *p* goes
to zero while *N**p* = *λ*.

The Poisson and exponential distributions are related. If the number of counts follows the Poisson distribution, then the interval between individual counts follows the exponential distribution.

The MLE and the MVUE of the Poisson parameter, *λ*,
is the sample mean. The sum of independent Poisson random variables
is also Poisson distributed with the parameter equal to the sum of
the individual parameters. This is used to calculate confidence intervals *λ*.
As *λ* gets large the Poisson distribution
can be approximated by a normal distribution with *µ* = *λ* and *σ*^{2} = *λ*.
This approximation is used to calculate confidence intervals for values
of *λ* greater than 100.

Compute and plot the pdf of a Poisson distribution with parameter `lambda = 5`

.

```
x = 0:15;
y = poisspdf(x,5);
plot(x,y,'+')
```

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