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25.7 Random Number Generation

Octave can generate random numbers from a large number of distributions. The random number generators are based on the random number generators described in Special Utility Matrices.

The following table summarizes the available random number generators (in alphabetical order).

Distribution

Function

Beta Distribution

betarnd

Binomial Distribution

binornd

Cauchy Distribution

cauchy_rnd

Chi-Square Distribution

chi2rnd

Univariate Discrete Distribution

discrete_rnd

Empirical Distribution

empirical_rnd

Exponential Distribution

exprnd

F Distribution

frnd

Gamma Distribution

gamrnd

Geometric Distribution

geornd

Hypergeometric Distribution

hygernd

Laplace Distribution

laplace_rnd

Logistic Distribution

logistic_rnd

Log-Normal Distribution

lognrnd

Pascal Distribution

nbinrnd

Univariate Normal Distribution

normrnd

Poisson Distribution

poissrnd

t (Student) Distribution

trnd

Univariate Discrete Distribution

unidrnd

Uniform Distribution

unifrnd

Weibull Distribution

wblrnd

Wiener Process

wienrnd

Function File: betarnd (a, b, r, c)
Function File: betarnd (a, b, sz)

Return an r by c or size (sz) matrix of random samples from the Beta distribution with parameters a and b. Both a and b must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of a and b.

Function File: binornd (n, p, r, c)
Function File: binornd (n, p, sz)

Return an r by c or a size (sz) matrix of random samples from the binomial distribution with parameters n and p. Both n and p must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of n and p.

Function File: cauchy_rnd (lambda, sigma, r, c)
Function File: cauchy_rnd (lambda, sigma, sz)

Return an r by c or a size (sz) matrix of random samples from the Cauchy distribution with parameters lambda and sigma which must both be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of lambda and sigma.

Function File: chi2rnd (n, r, c)
Function File: chi2rnd (n, sz)

Return an r by c or a size (sz) matrix of random samples from the chisquare distribution with n degrees of freedom. n must be a scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the size of n.

Function File: discrete_rnd (n, v, p)
Function File: discrete_rnd (v, p, r, c)
Function File: discrete_rnd (v, p, sz)

Generate a row vector containing a random sample of size n from the univariate distribution which assumes the values in v with probabilities p. n must be a scalar.

If r and c are given create a matrix with r rows and c columns. Or if sz is a vector, create a matrix of size sz.

Function File: empirical_rnd (n, data)
Function File: empirical_rnd (data, r, c)
Function File: empirical_rnd (data, sz)

Generate a bootstrap sample of size n from the empirical distribution obtained from the univariate sample data.

If r and c are given create a matrix with r rows and c columns. Or if sz is a vector, create a matrix of size sz.

Function File: exprnd (lambda, r, c)
Function File: exprnd (lambda, sz)

Return an r by c matrix of random samples from the exponential distribution with mean lambda, which must be a scalar or of size r by c. Or if sz is a vector, create a matrix of size sz.

If r and c are omitted, the size of the result matrix is the size of lambda.

Function File: frnd (m, n, r, c)
Function File: frnd (m, n, sz)

Return an r by c matrix of random samples from the F distribution with m and n degrees of freedom. Both m and n must be scalar or of size r by c. If sz is a vector the random samples are in a matrix of size sz.

If r and c are omitted, the size of the result matrix is the common size of m and n.

Function File: gamrnd (a, b, r, c)
Function File: gamrnd (a, b, sz)

Return an r by c or a size (sz) matrix of random samples from the Gamma distribution with parameters a and b. Both a and b must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of a and b.

See also: gamma, gammaln, gammainc, gampdf, gamcdf, gaminv.

Function File: geornd (p, r, c)
Function File: geornd (p, sz)

Return an r by c matrix of random samples from the geometric distribution with parameter p, which must be a scalar or of size r by c.

If r and c are given create a matrix with r rows and c columns. Or if sz is a vector, create a matrix of size sz.

Function File: hygernd (t, m, n, r, c)
Function File: hygernd (t, m, n, sz)
Function File: hygernd (t, m, n)

Return an r by c matrix of random samples from the hypergeometric distribution with parameters t, m, and n.

The parameters t, m, and n must positive integers with m and n not greater than t.

The parameter sz must be scalar or a vector of matrix dimensions. If sz is scalar, then a sz by sz matrix of random samples is generated.

Function File: laplace_rnd (r, c)
Function File: laplace_rnd (sz);

Return an r by c matrix of random numbers from the Laplace distribution. Or if sz is a vector, create a matrix of sz.

Function File: logistic_rnd (r, c)
Function File: logistic_rnd (sz)

Return an r by c matrix of random numbers from the logistic distribution. Or if sz is a vector, create a matrix of sz.

Function File: lognrnd (mu, sigma, r, c)
Function File: lognrnd (mu, sigma, sz)

Return an r by c matrix of random samples from the lognormal distribution with parameters mu and sigma. Both mu and sigma must be scalar or of size r by c. Or if sz is a vector, create a matrix of size sz.

If r and c are omitted, the size of the result matrix is the common size of mu and sigma.

Function File: nbinrnd (n, p, r, c)
Function File: nbinrnd (n, p, sz)

Return an r by c matrix of random samples from the Pascal (negative binomial) distribution with parameters n and p. Both n and p must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of n and p. Or if sz is a vector, create a matrix of size sz.

Function File: normrnd (m, s, r, c)
Function File: normrnd (m, s, sz)

Return an r by c or size (sz) matrix of random samples from the normal distribution with parameters mean m and standard deviation s. Both m and s must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of m and s.

Function File: poissrnd (lambda, r, c)

Return an r by c matrix of random samples from the Poisson distribution with parameter lambda, which must be a scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the size of lambda.

Function File: trnd (n, r, c)
Function File: trnd (n, sz)

Return an r by c matrix of random samples from the t (Student) distribution with n degrees of freedom. n must be a scalar or of size r by c. Or if sz is a vector create a matrix of size sz.

If r and c are omitted, the size of the result matrix is the size of n.

Function File: unidrnd (mx);
Function File: unidrnd (mx, v);
Function File: unidrnd (mx, m, n, …);

Return random values from discrete uniform distribution, with maximum value(s) given by the integer mx, which may be a scalar or multidimensional array.

If mx is a scalar, the size of the result is specified by the vector v, or by the optional arguments m, n, …. Otherwise, the size of the result is the same as the size of mx.

Function File: unifrnd (a, b, r, c)
Function File: unifrnd (a, b, sz)

Return an r by c or a size (sz) matrix of random samples from the uniform distribution on [a, b]. Both a and b must be scalar or of size r by c.

If r and c are omitted, the size of the result matrix is the common size of a and b.

Function File: wblrnd (scale, shape, r, c)
Function File: wblrnd (scale, shape, sz)

Return an r by c matrix of random samples from the Weibull distribution with parameters scale and shape which must be scalar or of size r by c. Or if sz is a vector return a matrix of size sz.

If r and c are omitted, the size of the result matrix is the common size of alpha and sigma.

Function File: wienrnd (t, d, n)

Return a simulated realization of the d-dimensional Wiener Process on the interval [0, t]. If d is omitted, d = 1 is used. The first column of the return matrix contains time, the remaining columns contain the Wiener process.

The optional parameter n gives the number of summands used for simulating the process over an interval of length 1. If n is omitted, n = 1000 is used.


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