The multinomial distribution is a generalization of the binomial distribution to multiple categories. It is perhaps easiest to think that we first extend a Bernoulli() distribution to include more than two categories, resulting in a Categorical() distribution. We then extend repeat the Categorical experiment several ($$n$$) times.

Multinomial(size, p)

## Arguments

size The number of trials. Must be an integer greater than or equal to one. When size = 1L, the Multinomial distribution reduces to the categorical distribution (also called the discrete uniform). Often called n in textbooks. A vector of success probabilities for each trial. p can take on any positive value, and the vector is normalized internally.

## Value

A Multinomial object.

## Details

We recommend reading this documentation on https://alexpghayes.github.io/distributions3, where the math will render with additional detail and much greater clarity.

In the following, let $$X = (X_1, ..., X_k)$$ be a Multinomial random variable with success probability p = $$p$$. Note that $$p$$ is vector with $$k$$ elements that sum to one. Assume that we repeat the Categorical experiment size = $$n$$ times.

Support: Each $$X_i$$ is in $${0, 1, 2, ..., n}$$.

Mean: The mean of $$X_i$$ is $$n p_i$$.

Variance: The variance of $$X_i$$ is $$n p_i (1 - p_i)$$. For $$i \neq j$$, the covariance of $$X_i$$ and $$X_j$$ is $$-n p_i p_j$$.

Probability mass function (p.m.f):

$$P(X_1 = x_1, ..., X_k = x_k) = \frac{n!}{x_1! x_2! ... x_k!} p_1^{x_1} \cdot p_2^{x_2} \cdot ... \cdot p_k^{x_k}$$

Cumulative distribution function (c.d.f):

Omitted for multivariate random variables for the time being.

Moment generating function (m.g.f):

$$E(e^{tX}) = (\sum_{i=1}^k p_i e^{t_i} )^n$$

Other discrete distributions: Bernoulli, Binomial, Categorical, Geometric, HyperGeometric, NegativeBinomial, Poisson

## Examples


set.seed(27)

X <- Multinomial(size = 5, p = c(0.3, 0.4, 0.2, 0.1))
X#> Multinomial distribution (size = 5, p = 0.3) Multinomial distribution (size = 5, p = 0.4) Multinomial distribution (size = 5, p = 0.2) Multinomial distribution (size = 5, p = 0.1)
random(X, 10)#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,]    5    5    5    5    5    5    5    5    5     5
# pdf(X, 2)
# log_pdf(X, 2)