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