| Edition |
5th ed. |
| Description |
xii, 310 p. : ill. |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Machine generated contents note: Preface; Introduction; Elements of Probability; Random Numbers; Generating Discrete Random Variables; Generating Continuous Random Variables; The Discrete Event Simulation Approach; Statistical Analysis of Simulated Data; Variance Reduction Techniques; Statistical Validation Techniques; Markov Chain Monte Carlo Methods; Some Additional Topics; Exercises; References; Index. |
| Summary |
"In formulating a stochastic model to describe a real phenomenon, it used to be that one compromised between choosing a model that is a realistic replica of the actual situation and choosing one whose mathematical analysis is tractable. That is, there did not seem to be any payoff in choosing a model that faithfully conformed to the phenomenon under study if it were not possible to mathematically analyze that model. Similar considerations have led to the concentration on asymptotic or steady-state results as opposed to the more useful ones on transient time. However, the relatively recent advent of fast and inexpensive computational power has opened up another approach--namely, to try to model the phenomenon as faithfully as possible and then to rely on a simulation study to analyze it"-- Provided by publisher. |
| Reproduction |
Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. |
| Subject |
Random variables.
|
|
Probabilities.
|
|
Computer simulation.
|
| Genre/Form |
Electronic books.
|
| Added Author |
ProQuest (Firm)
|
| ISBN |
9780124158252 (hardback) |
|
9780124159716 (electronic bk.) |
|
