nbinrnd

Negative binomial random numbers

Syntax

y = nbinrnd(r,p)

y = nbinrnd(r,p,sz1,...,szN)

y = nbinrnd(r,p,sz)

Description

y = nbinrnd(r,p) generates a random number drawn from the negative binomial distribution with the corresponding number of successes r and the probability of success in a single trial p.

y = nbinrnd(r,p,sz1,...,szN) generates an array of random numbers, where sz1,...,szN indicates the size of each dimension.

y = nbinrnd(r,p,sz) generates an array of random numbers, where the vector sz specifies size(r).

The simplest application of 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.

Examples

Generate Random Numbers from Negative Binomial Distribution

Generate 5000 random numbers from a negative binomial distribution with the corresponding number of successes r and probability of success per trial p.

rng(0,"twister") % For reproducibility
r = 6;
p = 0.1;
y = nbinrnd(r,p,[5000 1]);

Plot a histogram of the numbers.

histogram(y)

Input Arguments

r — Number of successes

positive scalar | array of positive scalars

Number of successes, specified as a positive scalar or an array of positive scalars.

To generate random numbers from multiple distributions, specify either r or p (or both) using arrays. If either r or p is an array, then the array sizes must be the same. In this case, nbinrnd expands the scalar input into a constant array of the same size as the array input.

Data Types: single | double

p — Probability of success

positive scalar in the range (0,1] | array of positive scalars in the range (0,1]

Probability of success, specified as a positive scalar or an array of positive scalars in the range (0,1].

To generate random numbers from multiple distributions, specify either r or p (or both) using arrays. If either r or p is an array, then the array sizes must be the same. In this case, nbinrnd expands the scalar input into a constant array of the same size as the array input.

Data Types: single | double

sz1,...,szN — Size of each dimension (as separate arguments)

integers

Size of each dimension, specified as separate arguments of integers. For example, specifying 5,3,2 generates a 5-by-3-by-2 array of random numbers from the probability distribution.

If either r or p is an array, then the specified dimensions sz1,...,szN must match the common dimensions of r and p after any necessary scalar expansion. The default values of sz1,...,szN are the common dimensions.

• If you specify a single value sz1, then y is a square matrix of size sz1-by-sz1.

• If the size of any dimension is 0 or negative, then y is an empty array.

• Beyond the second dimension, nbinrnd ignores trailing dimensions with a size of 1. For example, specifying 3,1,1,1 produces a 3-by-1 vector of random numbers.

Data Types: single | double

sz — Size of each dimension (as a row vector)

row vector of integers

Size of each dimension, specified as a row vector of integers. For example, specifying [5,3,2] generates a 5-by-3-by-2 array of random numbers from the probability distribution.

If either r or p is an array, then the specified dimensions sz must match the common dimensions of r and p after any necessary scalar expansion. The default values of sz are the common dimensions.

• If you specify a single value sz1, then y is a square matrix of size sz1-by-sz1.

• If the size of any dimension is 0 or negative, then y is an empty array.

• Beyond the second dimension, nbinrnd ignores trailing dimensions with a size of 1. For example, specifying [3,1,1,1] produces a 3-by-1 vector of random numbers.

Data Types: single | double

Output Arguments

y — Random numbers

scalar value | array of scalar values

Random numbers, returned as a scalar value or an array of scalar values with the dimensions specified by sz1,...,szN or sz. Each element in y is the random number generated from the distribution specified by the corresponding elements in r and p.

Alternative Functionality

• nbinrnd is a function specific to the negative binomial distribution. Statistics and Machine Learning Toolbox™ also offers the generic function random, which supports various probability distributions. To use random, specify the probability distribution name and its parameters. Note that the distribution-specific function nbinrnd is faster than the generic function random.

• To generate random numbers interactively, use randtool, a user interface for random number generation.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

The generated code can return a different sequence of numbers than MATLAB^® if either of the following is true:

• The output is nonscalar.

• An input parameter is invalid for the distribution.

For more information on code generation, see Introduction to Code Generation for Statistics and Machine Learning Functions and Overview of Code Generation Using MATLAB Coder (MATLAB Coder).

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced before R2006a