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Simulation Sheldon M. Ross

Simulation By Sheldon M. Ross

Simulation by Sheldon M. Ross

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$25.99
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Summary

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.

Simulation Summary

Simulation by Sheldon M. Ross

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.

Simulation Reviews

..".quite useful as a reference for the applied statistician."
--TECHNOMETRICS

Table of Contents

Part 1 Elements of probability: sample space and events; axioms of probability; conditional probability and independence; random variables; expectation; variance; Chebyshev's inequality and the laws of large numbers; some discrete random variables; continuous random variables; conditional expectation and conditional variance problems. Part 2 Random numbers: pseudo-random number generation; using random numbers to evaluate integrals. Part 3 Generating discrete random variables: the inverse transform method; generating a Poisson random variable; generating binomial random variables; the acceptance-rejection technique; the composition approach. Part 4 Generating continuous random variables: the inverse transform algorithm; the rejection method; the polar method for generating normal random variables; generating a Poisson process; generating a non-homogeneous Poisson process. Part 5 The discrete event simulation approach: simulation via discrete events; a single server queueing system; a queueing system with two servers in series; a queueing system with two parallel servers; an inventory model; a repair problem; exercising a stock option; verification of the simulation model problems. Part 6 Statistical analysis of simulated data: the sample means and sample variance; interval estimates of a population mean; the bootstrapping technique for estimating mean square errors. Part 7 Variance reduction techniques: the use of antipathetic variables; the use of control variates; variance reduction by conditioning; stratified sampling; importance sampling; using common random numbers. Part 8 Statistical validation techniques: goodness of fit tests; goodness of fit tests when some parameters are unspecified; the two-sample problem; validating the assumption of a nonhomogeneous Poisson process. Part 9 Markov chain Monte Carlo methods: Markov chains; the Hastings-Metropolis algorithm; the Gibbs sampler; simulated annealing; the sampling importance; resampling algorithm. Part 10 Some additional topics: the alias method for generating discrete random variables; simulating a two-dimensional Poisson process; simulation applications of an identity for sums of Bernoulli random variables; estimating probabilities and expected first passage times by using random hazards; appendix.

Additional information

GOR013600068
9780125984102
0125984103
Simulation by Sheldon M. Ross
Sheldon M. Ross
Statistical Modeling and Decision Science
Used - Good
Hardback
Elsevier Science Publishing Co Inc
1996-11-19
304
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Simulation