Simulation

Replete with easy-to-understand examples ranging from the prediction of home runs in baseball using an hierarchical Bayesian statistics model to estimating the expected return at blackjack using control variables, this text functions as a complete consideration of simulation. Sheldon Ross provides broad yet thorough coverage of the subject, presenting the development of a simulation study to analyze models, and demonstrates that by using random variables and the concept of discrete events, it is possible to generate the behavior of a stochastic model over time. Also discussed are questions concerning when to stop a simulation, how much confidence can be placed in the results, and extensive new information on the presentation of the alias method for generating discrete random variables material not found in any other text. Students, practitioners, and researchers alike will find this text to have an important place in their research libraries.


* Presents the statistics needed to analyze simulated data as well as those needed for validating the simulation model
* Stresses variance reduction, including control variables and their relation to regression analysis
* Includes a chapter on Markov chain monte carlo methods
* Emphasizes the use of computers throughout the text

Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research 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, and a recipient of the Humboldt US Senior Scientist Award.