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Simulation (Statistical Modeling and Decision Science)

Simulation (Statistical Modeling and Decision Science)

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Author: Sheldon M. Ross
Publisher: Academic Press
Category: Book

List Price: $82.95
Buy New: $18.00
You Save: $64.95 (78%)



New (8) Used (13) from $6.99

Avg. Customer Rating: 5.0 out of 5 stars 2 reviews
Sales Rank: 691566

Media: Hardcover
Edition: 2nd
Number Of Items: 1
Pages: 283
Shipping Weight (lbs): 1.1
Dimensions (in): 8.9 x 6.2 x 0.8

ISBN: 0125984103
Dewey Decimal Number: 519.2
EAN: 9780125984102
ASIN: 0125984103

Publication Date: November 1996
Availability: Usually ships in 1-2 business days

Also Available In:

  • Paperback - Simulation
  • Hardcover - Simulation, Third Edition
  • Digital - Simulation (Statistical Modeling and Decision Science)
  • Digital - Simulation, Third Edition

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Editorial Reviews:

Product Description
Replete with easy-to-understand examples ranging from the prediction of home runs in baseball using an hierarchical Bayesian statistics model to estimating the expected return at blackjack using control variables, this text functions as a complete consideration of simulation. Sheldon Ross provides broad yet thorough coverage of the subject, presenting the development of a simulation study to analyze models, and demonstrates that by using random variables and the concept of discrete events, it is possible to generate the behavior of a stochastic model over time. Also discussed are questions concerning when to stop a simulation, how much confidence can be placed in the results, and extensive new information on the presentation of the alias method for generating discrete random variables material not found in any other text. Students, practitioners, and researchers alike will find this text to have an important place in their research libraries.

* Presents the statistics needed to analyze simulated data as well as those needed for validating the simulation model
* Stresses variance reduction, including control variables and their relation to regression analysis
* Includes a chapter on Markov chain monte carlo methods
* Emphasizes the use of computers throughout the text



Customer Reviews:

5 out of 5 stars Excellent review of simulation techniques at an intermediate level.   August 21, 2007
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]



5 out of 5 stars A reader from Saudi Arabia   August 22, 1999
 26 out of 39 found this review helpful

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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