Page 120 - Computational Statistics Handbook with MATLAB

Chapter 4: Generating Random Variables 107

Another function that might prove useful in implementing computational
statistics methods is called randperm. This is provided with the standard
MATLAB software package, and it generates random permutations of the
integers 1 to n. The result can be used to permute the elements of a vector. For
example, to permute the elements of a vector x of size n, use the following
MATLAB statements:

% Get the permuted indices.
ind = randperm(n);
% Now re-order based on the permuted indices.
xperm = x(ind);
We also provide some functions in the Computational Statistics Toolbox for
generating random variables. These are outlined in Table 4.2. Note that these
generate random variables using the distributions as defined in Chapter 2.

T
A
T
A
4
4.2
.2
.2
E
B
B
E
L
T
TA AB BL LE L E4 4 .2
List of Functions from Chapter 4 Included in the Computational
Statistics Toolbox
Distribution MATLAB Function
Beta csbetarnd
Binomial csbinrnd
Chi-Square cschirnd
Discrete Uniform csdunrnd
Exponential csexprnd
Gamma csgamrnd
Multivariate Normal csmvrnd
Poisson cspoirnd
Points on a sphere cssphrnd

4.6 Further Reading

In this text we do not attempt to assess the computational efficiency of the
methods for generating random variables. If the statistician or engineer is
performing extensive Monte Carlo simulations, then the time it takes to gen-
erate random samples becomes important. In these situations, the reader is
encouraged to consult Gentle [1998] or Rubinstein [1981] for efficient algo-
rithms. Our goal is to provide methods that are easily implemented using
MATLAB or other software, in case the data analyst must write his own func-
tions for generating random variables from non-standard distributions.

© 2002 by Chapman & Hall/CRC