# Numpy Random Multinomial

NumPy Random Multinomial is a function in NumPy that generates a multinomial distribution.

In this article, we will discuss what a NumPy Random Multinomial Distribution is, its properties, and how to utilize it in NumPy.

## Multinomial Distribution

In Numpy random multinomial, multinomial probabilities are generalizations of binomial distributions.

A multinomial distribution is a probability distribution that describes the probabilities of a set of possible outcomes in a series of independent trials.

A multinomial scenario explains the results of multiple variables, in contrast to a binomial scenario which can only have two variables.

For example: The kind of blood in a population, the result of a dice roll.

### Syntax

`numpy.random.multinomial(n, pvals, size=None)`
 Parameters Overview n The number of possible results that can happen (for example, six possibilities for a dice roll). pvals This is a list of probabilities of results (for example. [1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 1/6] for a dice roll). size Determines the array’s shape.

Let’s look examples below to understand how the NumPy Random Multinomial function works.

Make a sample dice roll by drawing out the following:

#### Example:

from numpy import random dice = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6]) print(dice)

### Example Explanation

• The n parameter is set to 6, which means the dice will be rolled 6 times.
• The pvals parameter is set to a list of six equal probabilities, where each probability is 1/6, representing the probability of rolling each of the six faces of the dice.
• The random.multinomial() function generates a random sample of outcomes from a multinomial distribution, where each outcome represents the number of times each face of the dice was rolled in the given number of trials.
• The output of the function is an array of counts, where each element of the array represents the number of times each face of the dice was rolled in the 6 trials.
• The output of the code will be a random array of 6 integers, where each integer represents the count of times the corresponding face of the dice was rolled in the 6 trials.
• The sum of all the counts in the output array will be equal to the number of trials, which is 6 in this case.

You can draw out a sample of a coin flip as follows:

#### Example:

from numpy import random coin = random.multinomial(n=2, pvals=[1/2, 1/2]) print(coin)
Reminder: There is no one value generated by multinomial data! For each pval, they will generate a value.
Important: Since normal distributions are extensions of binomial distributions, their visual appearance and properties are identical to multiple binomial distributions.

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