Multinomial Distribution
In probability theory and statistics, the multinomial distribution is a generalization of the binomial distribution. It describes the probability of observing counts of each of several distinct outcomes, when the probability of each outcome is known. The multinomial distribution is often used in experimental design, where the goal is to determine the probability of observing a certain number of outcomes in a sample of size n. It is also used in machine learning, particularly in natural language processing, where it is used to model the frequency of words in a document or corpus. The multinomial distribution is characterized by its parameters, which are the probabilities of each outcome. These probabilities must sum to 1, and the number of trials must be specified. The mean and variance of the multinomial distribution can be calculated from its parameters.
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