geornd

Geometric random numbers

Syntax

r = geornd(p)
r = geornd(p,m,n,...)
r = geornd(p,[m,n,...])

Description

r = geornd(p) generates random numbers from a geometric distribution with probability parameter p. p can be a vector, a matrix, or a multidimensional array. The size of r is equal to the size of p. The parameters in p must lie in the interval [0,1].

r = geornd(p,m,n,...) or r = geornd(p,[m,n,...]) generates a multidimensional m-by-n-by-... array containing random numbers from the geometric distribution with probability parameter p. p can be a scalar or an array of the same size as r.

The geometric distribution is useful to model the number of failures before one success in a series of independent trials, where each trial results in either success or failure, and the probability of success in any individual trial is the constant p.

Examples

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Generate Random Numbers from Geometric Distribution

Generate a single random number from a geometric distribution with probability parameter p equal to 0.01.

rng default  % For reproducibility
p = 0.01;
r1 = geornd(0.01)
r1 =

    20

The returned random number represents a single experiment in which 20 failures were observed before a success, where each independent trial has a probability of success p equal to 0.01.

Generate a 1-by-5 array of random numbers from a geometric distribution with probability parameter p equal to 0.01.

r2 = geornd(p,1,5)
r2 =

     9   205     9    45   231

Each random number in the returned array represents the result of an experiment to determine the number of failures observed before a success, where each independent trial has a probability of success p equal to 0.01.

Generate a 1-by-3 array containing one random number from each of the three geometric distributions corresponding to the parameters in the 1-by-3 array of probabilities p.

p = [0.01 0.1 0.5];
r3 = geornd(p,[1 3])
r3 =

   127     5     0

Each element of the returned 1-by-3 array r3 contains one random number generated from the geometric distribution described by the corresponding parameter in P. For example, the first element in r3 represents an experiment in which 127 failures were observed before a success, where each independent trial has a probability of success p equal to 0.01. The second element in r3 represents an experiment in which 5 failures were observed before a success, where each independent trial has a probability of success p equal to 0.1. The third element in r3 represents an experiment in which zero failures were observed before a success - in other words, the first attempt was a success - where each independent trial has a probability of success p equal to 0.5.

See Also

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