- Hardcover: 304 pages
- Publisher: Academic Press Inc; 2nd Revised edition edition (19 Nov 1996)
- Language English
- ISBN-10: 0125984103
- ISBN-13: 978-0125984102
- Product Dimensions: 22.6 x 15.7 x 2 cm
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Amazon Bestsellers Rank:
2,053,827 in Books (See Top 100 in Books)
- #37 in Books > Science & Nature > Mathematics > Transformations
- #41 in Books > Scientific, Technical & Medical > Mathematics > Applied Mathematics > Stochastics
- See Complete Table of Contents
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More About the Author
Product Description
Product Description
Simulation allows complex real world situations to be analyzed quantitatively. First, a model is created to represent the situation, then, using probability and statistics theory, the computer can perform a simulation to predict the outcome of this situation. This text provides a description of the generation of random variables and their use in analyzing a model in simulation study. It details how a computer may be used to generate random numbers, which may then be used to generate the behaviour of a stochastic model over time. The statistics needed to analyze simulated data and to validate the simulation model are also presented.
About the Author
Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He received his Ph.D. in statistics at Stanford University in 1968 and has been at Berkeley ever since. 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, and a recipient of the Humboldt US Senior Scientist Award.
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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2 reviews
3 of 3 people found the following review helpful
Excellent review of simulation techniques at an intermediate level.
21 Aug 2007
Format:Hardcover
According to the books I have consulted by S. M. Ross, I consider him to
be a tremendous author, with this book being my absolute favourite. It
is inspirational, clear, creative and (most of all) fun to read.
Starting off from the very basics regarding probability, random numbers
and generation of random variables, Professor Ross steadily guides the
reader through, for instance, simulation with respect to some queueing
systems, validation techniques (e.g. goodness-of-fit tests;
Kolmogorov-Smirnov tests), the standard MCMC-methods and various variance
reduction techniques. The latter chapter is the one that I found most
enlightening including, for instance, a certainly rewarding section on
importance sampling (weighted simulation). Throughout, a lot of
well-chosen examples enhance students/readers understanding and interest
of the subject.
One might note that this is by no means the most advanced or complete book
on Monte Carlo simulation out there, see e.g.
Monte Carlo Statistical Methods (Springer Texts in Statistics)
or
Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability),
rather it evolves at an intermediate level covering a few more advanced
concepts as, for example, coupling from the past. But, having this in
mind, this is the book to get. Pure concentrated fun (xiii+274 pages).
Final note: In 2006 the fourth edition was launched. [Simulation, Fourth Edition]
be a tremendous author, with this book being my absolute favourite. It
is inspirational, clear, creative and (most of all) fun to read.
Starting off from the very basics regarding probability, random numbers
and generation of random variables, Professor Ross steadily guides the
reader through, for instance, simulation with respect to some queueing
systems, validation techniques (e.g. goodness-of-fit tests;
Kolmogorov-Smirnov tests), the standard MCMC-methods and various variance
reduction techniques. The latter chapter is the one that I found most
enlightening including, for instance, a certainly rewarding section on
importance sampling (weighted simulation). Throughout, a lot of
well-chosen examples enhance students/readers understanding and interest
of the subject.
One might note that this is by no means the most advanced or complete book
on Monte Carlo simulation out there, see e.g.
Monte Carlo Statistical Methods (Springer Texts in Statistics)
or
Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability),
rather it evolves at an intermediate level covering a few more advanced
concepts as, for example, coupling from the past. But, having this in
mind, this is the book to get. Pure concentrated fun (xiii+274 pages).
Final note: In 2006 the fourth edition was launched. [Simulation, Fourth Edition]
28 of 43 people found the following review helpful
A reader from Saudi Arabia
22 Aug 1999
Format:Hardcover
This is an excelent textbook explaining what simulation means and how to deepen your knowalge. You have to be a good programmer in order to use this book (and simulation generally)and the author should have added an andex for such a language and how its connection with simualtion ( C or C++) although my experience would elect MATLAB as prefernce!! This text does not requir any prior experience regarding simulatin although taking a course in statistics and probability would be advantageous!!
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