Multinomial Distribution
Multinomial Distribution
Multinomial distribution is a generalization of binomial distribution.
It describes outcomes of multi-nomial scenarios unlike binomial where scenarios must be only one of two.e.g. Blood type of a population, dice roll outcome.
It has three parameters:
n - number of times to run the experiment.
pvals - list of probabilties of outcomes (e.g. [1/6, 1/6, 1/6, 1/6, 1/6, 1/6] for dice roll).
size - The shape of the returned array.
Example
Draw out a sample for dice roll:
from numpy import random
x = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6])
print(x)
Try it Yourself »x = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6])
print(x)
Note: Multinomial samples will NOT produce a single value! They will produce one value for eachpval.
Note: As they are generalization of binomial distribution their visual representation and similarity of normal distribution is same as that of multiple binomial distributions.
