Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability)

Asmussen, Soren; Glynn, Peter W.

ISBN 10: 144192146X / ISBN 13: 9781441921468

Published by Springer Verlag, 2010

About this Item

Paperback, 476 pp. Small edge bump, else new.

Seller Inventory # 022505

Bibliographic Details

• Title: Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability)
• Publisher: Springer Verlag
• Publication Date: 2010
• Language: English
• ISBN 10: 144192146X
• ISBN 13: 9781441921468
• Binding: Paperback
• Condition: Fine
• Dust Jacket Condition: No Jacket
• Book Type: Book

About this title

Synopsis

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. It is the first rigorous and comprehensive advanced book on stochastic simulation. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. A large amount of exercises and illustrations are included, making the book of value to students, practitioners and researchers in a broad range of fields.

From the Back Cover

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms.

Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value.

Søren Asmussen is a professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is the Thomas Ford professor of Engineering at Stanford University.