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

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