- Story. Rare events occur with a rate per
unit time. There is no "memory" of previous events; i.e., that rate is
independent of time. A process that generates such events is called a
Poisson process. The occurrence of a rare event in this context is
referred to as an arrival. The number of arrivals in unit time is Poisson distributed.
- Example. The number of mutations in a strand of DNA per unit length (since mutations are rare) are Poisson distributed.
- Parameter. The single parameter is the rate of the rare events occurring.
- Support. The Poisson distribution is supported on the set of nonnegative integers.
- Probability mass function.
.
- Usage
Package |
Syntax |
NumPy |
np.random.poisson(lam) |
SciPy |
scipy.stats.poisson(lam) |
Stan |
poisson(lam) |
- Related distributions.
- In the limit of and such that the quantity is fixed, the Binomial distribution
becomes a Poisson distribution with parameter . Thus, for large and
small ,
,
with . Considering
the biological example of mutations, this is Binomially distributed:
There are bases, each with a probability of mutation, so the number
of mutations, n is binomially distributed. Since is small and is large, it is approximately Poisson distributed.
- Under the parametrization of the Negative Binomial distribution, taking the limit of large yields the Poisson distribution.
params = [dict(name='λ', start=1, end=20, value=5, step=0.1)]
app = distribution_plot_app(x_min=0,
x_max=40,
scipy_dist=st.poisson,
params=params,
x_axis_label='n',
title='Poisson')
bokeh.io.show(app, notebook_url=notebook_url)