Multinomial Distribution Multinomial Examples. Three card players play a series of matches. Categorical distribution is similar to the multinomical distribution expect for the output it produces.
In probability theory the multinomial distribution is a generalization of the binomial distribution for example it models the probability of counts for each side of a k sided die rolled n times. For example bayes rule can be used to predict the pressure of a system given the temperature and statistical data for the system. The probability that player a will win any game is 20 the probability that player b will win is 30 and the probability player c will win is 50.
The multinomial distribution can be used to compute the probabilities in situations in which there are more than two possible outcomes.
Multinomial and categorical infer the number of colors from the size of the probability vector p theta categorical data is in a form where the value tells the index of the color that was picked in a trial. The multinomial distribution models the probability of each combination of successes in a series of independent trials. So if n colors 5 categorical data. A binomial experiment will have a binomial distribution.
