This book systematically addresses the design and analysis of efficient
techniques for independent random sampling. Both general-purpose
approaches, which can be used to generate samples from arbitrary
probability distributions, and tailored techniques, designed to
efficiently address common real-world practical problems, are introduced
and discussed in detail. In turn, the monograph presents fundamental
results and methodologies in the field, elaborating and developing them
into the latest techniques. The theory and methods are illustrated with
a varied collection of examples, which are discussed in detail in the
text and supplemented with ready-to-run computer code.
The main problem addressed in the book is how to generate independent
random samples from an arbitrary probability distribution with the
weakest possible constraints or assumptions in a form suitable for
practical implementation. The authors review the fundamental results and
methods in the field, address the latest methods, and emphasize the
links and interplay between ostensibly diverse techniques.