
MULTINOMIAL PDFName:
The multinomial distribution extends this by allowing k possible outcomes. These outcomes are mutually exclusive with each outcome having probability p_{i} The p_{i} must sum to 1 and are the same for each trial. The multinomial distribution is the probability that each event occurs x_{i} times (i = 1, 2, ..., k) in the n trials. The probability mass function for the multinomial distribution is defined as
where x_{1} ..., x_{k} are nonnegative integers that sum to the number of trials and the p_{i} denote the probabilities of outcome i. The p_{i} should all be in the interval (0,1) and sum to 1.
where <x> is a nonnegative variable specifying the number of times the corresponding outcome occurs; <p> is a variable (of the same length as <x>) containing the desired probabilities for each outcome; and where <a> is a parameter where the resulting multinomial pdf is stored.
LET X = DATA 5 4 10 8 7 LET A = MULTINOMIAL PDF X P
let p = data 0.2 0.1 0.2 0.3 0.2 let x = data 12 5 8 10 6 . let a = multinomial pdf x p . print aThe computed value of a is 0.0002189.
Date created: 7/7/2004 