Probability Distributions > Multinomial Distribution
The multinomial distribution is used to find probabilities in experiments where there are more than two outcomes.
Binomial vs. Multinomial Experiments
- Fixed number of n trials.
- Each trial is an independent event.
- Only two outcomes are possible (Success and Failure).
- Probability of success (p) for each trial is constant.
- A random variable Y= the number of successes.
A multinomial experiment is almost identical with one main difference: a binomial experiment can have two outcomes, while a multinomial experiment can have multiple outcomes.
Example: You roll a die ten times to see what number you roll. There are 6 possibilities (1,2,3,4,5,6), so this is a multinomial experiment. If you rolled the die ten times to see how many times you roll a three, that would be a binomial experiment (3 = success, 1,2,4,5,6 = failure).
A binomial experiment will have a binomial distribution. A multinomial experiment will have a multinomial distribution.
Multinomial Distribution Example
Three card players play a series of matches. 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%. If they play 6 games, what is the probability that player A will win 1 game, player B will win 2 games, and player C will win 3?
- n = number of events
- n1 = number of outcomes, event 1
- n2 = number of outcomes, event 2
- n3 = number of outcomes, event x
- p1 = probability event 1 happens
- p2 = probability event 2 happens
- px = probability event x happens
Using the data from the question, we get:
- n = 12 (6 games total).
- n1 = 1 (Player A wins).
- n2 = 2 (Player B wins).
- n3 = 3 (Player C wins).
- p1 = 0.20 (probability that Player A wins).
- p2 = 0.30 (probability that Player B wins).
- p3 = 0.50 (probability that Player C wins).
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