Simulación (2a edición)

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. 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 This text provides a description of the generation of random variables and their use in analyzing a model in simulation study. It details how a computer may be used to generate random numbers, which may then be used to generate the behaviour of a stochastic model over time. This text shows how to analyze a model by use of a simulation study