This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.


Negative binomial random numbers


RND = nbinrnd(R,P)
RND = nbinrnd(R,P,m,n,...)
RND = nbinrnd(R,P,[m,n,...])


RND = nbinrnd(R,P) is a matrix of random numbers chosen from a negative binomial distribution with corresponding number of successes, R and probability of success in a single trial, P. R and P can be vectors, matrices, or multidimensional arrays that have the same size, which is also the size of RND. A scalar input for R or P is expanded to a constant array with the same dimensions as the other input.

RND = nbinrnd(R,P,m,n,...) or RND = nbinrnd(R,P,[m,n,...]) generates an m-by-n-by-... array. The R, P parameters can each be scalars or arrays of the same size as R.

The simplest motivation for the negative binomial is the case of successive random trials, each having a constant probability P of success. The number of extra trials you must perform in order to observe a given number R of successes has a negative binomial distribution. However, consistent with a more general interpretation of the negative binomial, nbinrnd allows R to be any positive value, including nonintegers.


Suppose you want to simulate a process that has a defect probability of 0.01. How many units might Quality Assurance inspect before finding three defective items?

r = nbinrnd(3,0.01,1,6)+3
r =
  496  142  420  396  851  178

Extended Capabilities

Introduced before R2006a

Was this topic helpful?