- Sheldon Ross, University of Southern California, Los Angeles, USA
This latest edition features all-new material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables.
By explaining 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, Rossâs Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.
AudienceSenior/graduate level students taking a course in Simulation, found in many different departments, including: Computer Science, Industrial Engineering, Operations Research, Statistics, Mathematics, Electrical Engineering, and Quantitative Business Analysis.
- Published: October 2012
- Imprint: ACADEMIC PRESS
- ISBN: 978-0-12-415825-2
"I have always liked Rossâ books, as he is simultaneously mathematically rigorous and very interested in applications. The biggest strength I see is the rare combination of mathematical rigor and illustration of how the mathematical methodologies are applied in practice. Books with practical perspective are rarely this rigourous and mathematically detailed. I also like the variety of exercises, which are quite challenging and demanding excellence from students."
--Prof. Krzysztof Ostaszewski, Illinois State University.
Table of Contents
Chapter 1 - Introduction
Chapter 2 - Elements of Probability
Chapter 3 - Random Numbers
Chapter 4 - Generating Discrete Random Variables
Chapter 5 - Generating Continuous Random Variables
Chapter 6 - The Multivariate Normal Distribution and Copulas
Chapter 7 - The Discrete Event Simulation Approach
Chapter 8 - Statistical Analysis of Simulated Data
Chapter 9 - Variance Reduction Techniques
Chapter 10 - Additional Variance Reduction Techniques
Chapter 11 - Statistical Validation Techniques
Chapter 12 - Markov Chain Monte Carlo Methods