This is a very interesting book for all who are interested in stochastic simulations. The book is divided into two parts. While the first part focuses on general methods, such as generating random variables, output analysis, steady state simulations, variance reduction methods, rare events simulations, derivative estimation and stochastic optimization, the second half discusses model specific algorithms, such as numerical integration via simulations, stochastic differential equations, generation of Gaussian and Levy processes and MCMC. The level of mathematical discussion is different from that presented elsewhere. A quite broad knowledge of probability and statistics is expected. According to the authors, the book is designed as a potential teaching and learning tool for use in a wide variety of courses. I am afraid that it is intended rather for PhD students than for Masters level due to the fact that many topics are only touched upon and a considerable effort is often needed to move from the ideas raised in the book to the well and efficiently working algorithms. I had a feeling in some parts of the book that it is a series of remarks intended more for the authors than for the readers. However, I am convinced that it is a book that should be on the bookshelf of everybody who is seriously interested in stochastic simulations.