## bbrand

This function is not yet fully documented. This is a transcript of
the text-formatted help.

bbrand Generate pseudo-random variates given a distribution.
X=bbrand generates a uniformly distributed variates, e.g. a number
distributed equally between 0<=X<=1. bbrand(M) creates a column-vector
of length M, and bbrand([M,N]) creates an M-by-N matrix.
bbrand is a utility-function intended to make BBTools self-contained.
If more functionality is required, please consider acquiring the
statistics toolbox from Mathworks.
bbrand(M,'dist',...) or bbrand('dist',...) selects another, possibly
parametrized, distribution. Parameters may either by a scalar, used
for all elements, or a matrix of the same size as X.
The following lists the available continuous distributions. The number
in parenthesis following a parameter is the default value. Densities
outside the specified domain are zero.
'uniform': Parameters: a (0), b (1)
Density: f(x)=1/(b-a), a<=x<=b
'normal': Parameters: mu (0), sigma (1)
Density: f(x)=exp(-(x-mu)^2/(2*sigma^2))/(c*sigma)
c=sqrt(2*pi)
'exp': Parameter: theta (1)
Density: f(x)=exp(-x/theta)/theta, x>=0
'cauchy': Parameters: mu (0), sigma (1)
Density: f(x)=sigma/(pi*((x-mu)^2+sigma^2))
Other names: 'lorentz', 'breit-wigner'.
'pareto': Parameters: a (1), b (1)
Density: f(x)=a*b^a/x^(a+1), x>=a
Other names: 'bradford'
'rayleigh': Parameter: s (1)
Density: f(x)=x*exp(-.5*(x/s)^2)/s^2, x>=0
NOTE: the mean and variance of the Cauchy distribution is undefined.
We use mu and sigma as parameters because they reflect our intuition
about the shape of the distribution.
Support for discrete distributions is currently limited. In the
following, i is always a non-negative integer.
'geometric': Parameter: p (1)
Density: (1-p)^(i-1)*p, i>=1
'poisson': Parameter: lambda (1)
Density: P(x==i)=exp(-lambda)*lambda^i/(i!), i>=0
Parameters can often be derived from the standard parameters: mean
('mean' or 'mu'), standard deviation ('std' or 'sigma'), or variance
('var'). The call X=bbrand(10,'exp','mu',10) creates 10 variates drawn
an exponential distribution with mean 10.
All distributions are based on the random functions built into
Matlab. Reproducible results may therefore be generated by seeding.
Please consult documentation for rand for details.
See also , .

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