This is an old version, view current version.

15.1 Multinomial distribution

15.1.1 Probability mass function

If KN, NN, and θK-simplex, then for yNK such that Kk=1yk=N, \text{Multinomial}(y|\theta) = \binom{N}{y_1,\ldots,y_K} \prod_{k=1}^K \theta_k^{y_k}, where the multinomial coefficient is defined by \binom{N}{y_1,\ldots,y_k} = \frac{N!}{\prod_{k=1}^K y_k!}.

15.1.2 Sampling statement

y ~ multinomial(theta)

Increment target log probability density with multinomial_lupmf(y | theta).

15.1.3 Stan functions

real multinomial_lpmf(int[] y | vector theta)
The log multinomial probability mass function with outcome array y of size K given the K-simplex distribution parameter theta and (implicit) total count N = sum(y)

real multinomial_lupmf(int[] y | vector theta)
The log multinomial probability mass function with outcome array y of size K given the K-simplex distribution parameter theta and (implicit) total count N = sum(y) dropping constant additive terms

int[] multinomial_rng(vector theta, int N)
Generate a multinomial variate with simplex distribution parameter theta and total count N; may only be used in transformed data and generated quantities blocks