   Evolution is progress—
progress is creativity.  # Poisson Distribution

Though this article tackles Poisson distribution, a type of discrete probability distribution, it is rather focused on practical questions how to use matplotlib to construct graphs of that distribution.

Numpy offers three parameters that effect graphical appearance:

parameter meaning usage
mu shape parameter essential, to construct a probability distribution
loc origin optional, the origin of the probability distribution default = 0
size vector length optional, size of the one dimensional array of random variables default=0 => no array but a single random variable is returned
[Error: Macro 'mathplot' error: mathplot() got an unexpected keyword argument 'title']

The probability mass function can be written:

[Error: Macro 'me' doesn't exist]

The cumulative probability function can be described by the equation:

[Error: Macro 'me' doesn't exist]

The function of the Numpy parameters can be tested by the following sage cell.

[Error: Macro 'sagecell' doesn't exist]

(c) Mato Nagel, Weißwasser 2004-2023, Disclaimer