A simulation that has any random aspects involves sampling, or
generating random variates from probability distributions. There are
situations in the practice of statistical research where continuous
distributions are not characterized by their density or cumulative
distribution function. Constructing such algorithms is a special problem
of random variate generation. The objective of this work is to
implement, test and improve these algorithms. It is investigated whether
such algorithms in the literature can be used in practice, namely, in
simulation. In the first part, the book provides a basic introduction to
random variate generation. Then the testing methods for random variate
generation algorithms used in this book are formulated. Afterwards, it
is described how to generate random variates if only the Fourier
coefficients of the desired distribution are known. It then deals with
generating random variates from a distribution where just a few moments
are known. The next part includes five different algorithms about
generation of vectors where only correlation and marginal distribution
are known. In the end, C codes of all algorithms in the book are given.