Simulation

6. Auflage 2022
ISBN: 978-0-323-85739-0
Verlag: Elsevier Science & Technology

Buch, Englisch, 336 Seiten, Format (B × H): 159 mm x 236 mm, Gewicht: 668 g

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.

Zielgruppe

Students at the advanced undergraduate / early graduate level taking a course in Simulation, found in many different departments, including: Computer Science, Industrial Engineering, Operations Research, Statistics, Mathematics, Electrical Engineering, and Quantitative Business Analysis.
Researchers/Professionals

Autoren/Hrsg.

Ross, Sheldon M.

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation
• Technische Wissenschaften Technik Allgemein Ingenieurwissenschaftliches Knowhow
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie

Inhaltsverzeichnis

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

Über Autor(innen)

Ross, Sheldon M.
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.