The 5th edition of Ross's Simulation continues to introduce 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 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, Ross's Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
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