Multinomial distribution

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In probability theory, the multinomial distribution is a generalization of the binomial distribution. The binomial distribution is the probability distribution of the number of "successes" in n independent Bernoulli trials, with the same probability of "success" on each trial. Instead of each trial resulting in "success" or "failure", imagine that each trial results in one of some fixed finite number k of possible outcomes, with probabilities p₁, ..., p_k, and there are n independent trials. We can use a random variable X_i to indicate the number of times outcome number i was observed over the n trials. Then, the multinomial distribution can be defined as the distribution of the vector

[(X_1,\dots,X_k) \,]

The probabilities are given by

[P(X_1=x_1,\dots,X_k=x_k)=\beginp_1^\cdots p_k^ \quad & \mbox \sum_^k x_i=n \\0 & \mbox \end]

for non-negative integers x₁, ..., x_k.

Each of the k components separately has a binomial distribution with parameters n and p_i, for the appropriate value of the subscript i, and, because of the constraint that the sum of the components is n, they are negatively correlated.

The expected value is

[\operatorname(X_i) = n p_i.]

The covariance matrix is as follows. Each diagonal entry is the variance of a binomially distributed random variable, and is therefore

[\operatorname(X_i)=np_i(1-p_i).]

The off-diagonal entries are the covariances. These are

[\operatorname(X_i,X_j)=-np_i p_j]

for i, j distinct. This is a k × k nonnegative-definite matrix of rank k − 1.

The off-diagonal entries of the corresponding correlation matrix are:

[\rho(X_i,X_j) = -\sqrt}]

Note that the sample size drops out of this expression. All off-diagonal entries are negatively correlated because for fixed N, an increase in one component of a multinomial vector requires a decrease in another component.

The Dirichlet distribution is the conjugate prior of the multinomial in Bayesian statistics.

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Multinomial distribution

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