Sheldon M. Ross

Simulation (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Simulation, Sixth Edition 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 will learn to apply the results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions. 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, this book presents the statistics needed to analyze simulated data and validate simulation models.

• Includes updated content throughout
• Offers a wealth of practice exercises as well as applied use of free software package R
• Features the author's well-known, award-winning and accessible approach to complex information

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 336
• Erscheinungstermin: 14. Juni 2022
• Englisch
• ISBN-13: 9780323899611
• Artikelnr.: 64183715

Autorenporträt

Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD 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.

Inhaltsangabe

1. Introduction 2. Elements of Probability 3. Random Numbers 4. Generating
Discrete Random Variables 5. Generating Continuous Random Variables 6. The
Multivariate Normal Distribution and Copulas 7. The Discrete Event
Simulation Approach 8. Statistical Analysis of Simulated Data 9. Variance
Reduction Techniques 10. Additional Variance Reduction Techniques 11.
Statistical Validation Techniques 12. Markov Chain Monte Carlo Methods