Multinomial distribution is a generalization of binomial distribution.
It describes outcomes of multi-nominal 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 probabilities 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.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)Note:Multinomial samples will NOT produce a single value! They will produce one value for each pval.
Note:As they are generalization of binomial distribution their visual representation and similarity of normal distribution is same as that of multiple binomial distributions.