Probability Distributions and their Stories
Discrete multivariate distributions
So far, we have looked a univariate distributions, but we will consider multivariate distributions in class, and you will encounter them in your research. First, we consider a discrete multivariate distribution, the Multinomial.
Multinomial distribution
- Story. This is a generalization of the Binomial distribution. Instead of a Bernoulli trial consisting of two outcomes, each trial has
outcomes. The probability of getting
of outcome 1,
of outcome 2, ..., and
of outcome
out of a total of
trials is Multinomially distributed.
- Example. There are two alleles in a population, A and a. Each individual may have genotype AA, Aa, or aa. The probability distribution describing having
AA individuals,
Aa individuals, and
aa individuals in a population of
total individuals is Multinomially distributed.
- Parameters.
, the total number of trials, and
, the probabilities of each outcome. Note that
and there is a further restriction that
.
- Support. The K-nomial distribution is supported on
.
- Usage The usage below assumes that theta is a length K array.
Package Syntax NumPy np.random.multinomial(N, theta)
SciPy scipy.stats.multinomial(N, theta)
Stan sampling multinomial(theta)
Stan rng multinomial_rng(theta, N)
- Probability density function.
- Related distributions.
- The Multinomial distribution generalizes the Binomial distribution to multiple dimensions.
- Notes.
- For a sampling statement in Stan, the value of N is implied