Foundations and Methods of Stochastic Simulation: A First Course
Nelson, Barry L., Pei, Linda
- 出版商: Springer
- 出版日期: 2021-11-11
- 售價: $4,780
- 貴賓價: 9.5 折 $4,541
- 語言: 英文
- 頁數: 313
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3030861937
- ISBN-13: 9783030861933
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相關分類:
機率統計學 Probability-and-statistics
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相關主題
商品描述
This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material); and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems and prepares the reader to use simulation in research as well as practice.
The book is a rigorous, but concise treatment, emphasizing lasting principles but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this. An online solutions manual for end of chapter exercises is also be provided.
作者簡介
Barry L. Nelson is the Walter P. Murphy Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University, US. His research expertise is in the design and analysis of computer simulation experiments on models of stochastic systems, focusing particularly on statistical efficiency and simulation optimization. His application domains include computer-performance modelling, manufacturing systems, financial engineering and transportation. He is a Fellow of INFORMS and IISE.
Linda Pei is a senior Ph.D. student in the Department of Industrial Engineering and Management Sciences at Northwestern University, US. Her research interests are simulation optimization and data science. She designed and developed Python programs for large-scale parallel simulation optimization and was named the Outstanding Teaching Assistant in the department.
