Since the publication in 1981 of Simulation and the Monte Carlo Method, dramatic changes
have taken place in the entire field of Monte Carlo simulation. This long-awaited second
edition gives a fully updated and comprehensive account of the major topics in Monte Carlo
simulation.
The book is based on an undergraduate course on Monte Carlo methods given at the
Israel Institute of Technology (Technion) and the University of Queensland for the past five
years. It is aimed at a broad audience of students in engineering, physical and life sciences,
statistics, computer science and mathematics, as well as anyone interested in using Monte
Carlo simulation in his or her study or work. Our aim is to provide an accessible introduction
to modern Monte Carlo methods, focusing on the main concepts while providing a sound
foundation for problem solving. For this reason, most ideas are introduced and explained
via concrete examples, algorithms, and experiments.
Although we assume that the reader has some basic mathematical knowledge, such as
gained from an elementary course in probability and statistics, we nevertheless review the
basic concepts of probability, Markov processes, and convex optimization in Chapter 1.
In a typical stochastic simulation, randomness is introduced into simulation models via
independent uniformly distributed random variables. These random variables are then used
as building blocks to simulate more general stochastic systems. Chapter 2 deals with the
generation of such random numbers, random variables, and stochastic processes.