05615nam 22007574a 450 991114445020332120200520144314.097866125401589781282540156128254015797800805172230080517226(CKB)1000000000707629(EBL)546638(OCoLC)662456957(SSID)ssj0000428180(PQKBManifestationID)12146683(PQKBTitleCode)TC0000428180(PQKBWorkID)10424434(PQKB)11180012(MiAaPQ)EBC546638(Au-PeEL)EBL546638(CaPaEBR)ebr10371749(CaONFJC)MIL254015(PPN)170253295(FR-PaCSA)41001523(FRCYB41001523)41001523(EXLCZ)99100000000070762920060524d2006 uy 0engurcn|||||||||rdacontentrdamediardacarrierSimulation /Sheldon M. Ross4th ed.Amsterdam ;Boston Elsevier Academic Pressc20061 recurso en línea (313 p.)Statistical Modeling and Decision ScienceDescription based upon print version of record.9780125980630 0125980639 Includes bibliographical references and index.Front Cover; Title Page; Copyright page; Table of Contents; Preface; Overview; New to This Edition; Chapter Descriptions; Thanks; 1. Introduction; Exercises; 2. Elements of Probability; 2.1 Sample Space and Events; 2.2 Axioms of Probability; 2.3 Conditional Probability and Independence; 2.4 Random Variables; 2.5 Expectation; 2.6 Variance; 2.7 Chebyshev's Inequality and the Laws of Large Numbers; 2.8 Some Discrete Random Variables; Binomial Random Variables; Poisson Random Variables; Geometric Random Variables; The Negative Binomial Random Variable; Hypergeometric Random Variables2.9 Continuous Random Variables Uniformly Distributed Random Variables; Normal Random Variables; Exponential Random Variables; The Poisson Process and Gamma Random Variables; The Nonhomogeneous Poisson Process; 2.10 Conditional Expectation and Conditional Variance; The Conditional Variance Formula; Exercises; References; 3. Random Numbers; Introduction; 3.1 Pseudorandom Number Generation; 3.2 Using Random Numbers to Evaluate Integrals; Exercises; References; 4. Generating Discrete Random Variables; 4.1 The Inverse Transform Method; 4.2 Generating a Poisson Random Variable4.3 Generating Binomial Random Variables 4.4 The Acceptance-Rejection Technique; 4.5 The Composition Approach; 4.6 Generating Random Vectors; Exercises; 5. Generating Continuous Random Variables; Introduction; 5.1 The Inverse Transform Algorithm; 5.2 The Rejection Method; 5.3 The Polar Method for Generating Normal Random Variables; 5.4 Generating a Poisson Process; 5.5 Generating a Nonhomogeneous Poisson Process; Exercises; References; 6. The Discrete Event Simulation Approach; Introduction; 6.1 Simulation via Discrete Events; 6.2 A Single-Server Queueing System6.3 A Queueing System with Two Servers in Series 6.4 A Queueing System with Two Parallel Servers; 6.5 An Inventory Model; 6.6 An Insurance Risk Model; 6.7 A Repair Problem; 6.8 Exercising a Stock Option; 6.9 Verification of the Simulation Model; Exercises; References; 7. Statistical Analysis of Simulated Data; Introduction; 7.1 The Sample Mean and Sample Variance; 7.2 Interval Estimates of a Population Mean; 7.3 The Bootstrapping Technique for Estimating Mean Square Errors; Exercises; References; 8. Variance Reduction Techniques; Introduction; 8.1 The Use of Antithetic Variables8.2 The Use of Control Variates 8.3 Variance Reduction by Conditioning; Estimating the Expected Number of Renewals by Time t; 8.4 Stratified Sampling; 8.5 Applications of Stratified Sampling; Analyzing Systems Having Poisson Arrivals; Computing Multidimensional Integrals of Monotone Functions; Compound Random Vectors; 8.6 Importance Sampling; 8.7 Using Common Random Numbers; 8.8 Evaluating an Exotic Option; 8.9 Estimating Functions of Random Permutations and Random Subsets; Random Permutations; Random Subsets8.10 Appendix: Verification of Antithetic Variable Approach When Estimating the Expected Value of Monotone FunctionsRoss's Simulation, Fourth Edition 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 text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presentsStatistical Modeling and Decision ScienceRandom variablesProbabilitiesComputer simulationRandom variables.Probabilities.Computer simulation.519.2Ross Sheldon M20432MiAaPQMiAaPQMiAaPQBOOK9911144450203321Simulation748469UNINA