Ross's Simulation, Fourth Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes.
This text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.

New to this Edition:
-More focus on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis
-A chapter on Markov chain monte carlo methods with many examples
-Unique material on the alias method for generating discrete random variables

Customer Rating:
Summary: An Introduction to Monte Carlo Simulation
Comment: "Simulation" by Sheldon Ross is a good book for introducing undergraduates to Monte Carlo simulation. I teach statistics to final year undergraduate students and I find that this book is at the perfect level for my students.

After providing a recap of the basics of probability theory, Ross defines what a random number and a pseudorandom number are and then details how these numbers can be used to generate random variates from discrete and continuous probability distributions. Ross discusses the most commonly used algorithms for generating such variates, including the Inverse Transformation Method, the Acceptance-Rejection Method and methods for generating normal random variates. Ross also discusses problem solving using a simulation approach; the analysis of simulated data; variance reduction techniques (including how to determine the number of simulations required in order to solve a problem); and Markov Chain Monte Carlo methods.

Ross's explanations of the various topics are simple to follow and include a large number of worked examples to illustrate the various points and formulae. A large number of exercises are also provided at the end of each chapter, although solutions are not given.

The focus of this book is entirely on Monte Carlo simulation methods and Ross does not touch on any of the more sophisticated methods that have now superseded Monte Carlo methods (such as Latin Hypercube sampling), which may make this book a bit too simplistic for more advanced students (I am also writing a PhD in statistics, that involves the use of simulation techniques, and I found this book to be too basic to be helpful for that). However, for statistics students who are taking their first course in simulation, this book is perfectly pitched.

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