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008			050916s2002 caua b 001 0 eng
010			_a 2001096358
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040			_aDLC _cDLC _dDLC
050	0	0	_aQA273 _b.R82 2002
069			_a04576119
090			_aQA 273 .R82 2002
090			_aQA 273 .R82 2002
100	1		_aRoss, Sheldon M. _979986
245	1	0	_aSimulation / _cSheldon M. Ross.
250			_a3rd ed.
260			_aSan Diego : _bAcademic Press, _cc2002.
300			_axiii, 274 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
505	0		_aElements 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 -- Binomial Random Variables -- Poisson Random Variables -- Geometric Random Variables -- The Negative Binomial Random Variable -- Hypergeometric Random Variables -- Continuous Random Variables -- Uniformly Distributed Random Variables -- Normal Random Variables -- Exponential Random Variables -- The Poisson Process and Gamma Random Variables -- The Nonhomogeneous Poisson Process -- Conditional Expectation and Conditional Variance -- Random Numbers -- Pseudorandom Number Generation -- Using Random Numbers to Evaluate Integrals -- Generating Discrete Random Variables -- The Inverse Transform Method -- Generating a Poisson Random Variable -- Generating Binomial Random Variables -- The Acceptance-Rejection Technique -- The Composition Approach -- Generating Random Vectors -- 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 Nonhomogeneous Poisson Process -- 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 -- An Insurance Risk Model -- A Repair Problem -- Exercising a Stock Option -- Verification of the Simulation Model -- Statistical Analysis of Simulated Data -- The Sample Mean and Sample Variance -- Interval Estimates of a Population Mean -- The Bootstrapping Technique for Estimating Mean Square Errors -- Variance Reduction Techniques -- The Use of Antithetic Variables -- The Use of Control Variates -- Variance Reduction by Conditioning -- Estimating the Expected Number of Renewals by Time t -- Stratified Sampling -- Importance Sampling -- Using Common Random Numbers -- Evaluating an Exotic Option -- Verification of Antithetic Variable Approach When Estimating the Expected Value of Monotone Functions -- Statistical Validation Techniques -- Goodness of Fit Tests -- The Chi-Square Goodness of Fit Test for Discrete Data -- The Kolmogorov-Smirnov Test for Continuous Data -- Goodness of Fit Tests When Some Parameters Are Unspecified -- The Discrete Data Case -- The Continuous Data Case -- The Two-Sample Problem -- Validating the Assumption of a Nonhomogeneous Poisson Process -- Markov Chain Monte Carlo Methods -- Markov Chains -- The Hastings-Metropolis Algorithm -- The Gibbs Sampler -- Simulated Annealing -- The Sampling Importance Resampling Algorithm -- 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 the Distribution and the Mean of the First Passage Time of a Markov Chain -- Coupling from the Past.
520		0	_aSheldon Ross' < I> Simulation, Third Edition< /I> introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This new adaptation of Sheldon Ross' best-selling Simulation provides a comprehensive, in-depth, and current guide for constructing probability models and simulations for a variety of purposes. This edition features new information, including the presentation of the Insurance Risk Model, generating a Random Vector, and evaluating an Exotic Option. Also new is coverage of the changing nature of statistical methods in practice due to the advancements in computing technology.
650		0	_aRandom variables. _957586
650		0	_aProbabilities. _911067
650		0	_aComputer simulation. _979987
852			_9p77.95 _y05-15-2004
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999			_c15279 _d15279