A COURSE IN MONTE CARLO is a concise explanation of the Monte Carlo (MC) method. In addition to providing guidance for generating samples from diverse distributions, it describes how to design, perform, and analyze the results of MC experiments based on independent replications, Markov chain MC, and MC optimization. The text gives considerable emphasis to the variance-reducing techniques of importance sampling, stratified sampling, Rao-Blackwellization, control variates, antithetic variates, and quasi-random numbers. For solving optimization problems it describes several MC techniques, including simulated annealing, simulated tempering, swapping, stochastic tunneling, and genetic algorithms. Examples from many areas show how these techniques perform in practice. Hands-on exercises enable student to experience challenges encountered when solving real problems. An answer key to selected problems is included.
George Fishman has regularly contributed to the literature on the Monte Carlo method and discrete-event simulation over the last 40 years. His earlier book, MONTE CARLO: CONCEPTS, ALGORITHMS, AND APPLICATIONS (Springer-Verlag, 1996) won the 1996 Lancaster award for best publication of the Institute for Operations Research and Management Sciences (INFORMS) and the 1997 outstanding publication award of the INFORMS College on Simulation.