LEADER 05468nam 2200685Ia 450 001 9910583359203321 005 20230120004533.0 010 $a1-280-63598-3 010 $a9786610635986 010 $a0-08-046476-9 024 3 $z9780444514288 035 $a(CKB)1000000000358118 035 $a(EBL)274681 035 $a(OCoLC)476019961 035 $a(SSID)ssj0000246149 035 $a(PQKBManifestationID)11188897 035 $a(PQKBTitleCode)TC0000246149 035 $a(PQKBWorkID)10180359 035 $a(PQKB)11022490 035 $a(MiAaPQ)EBC274681 035 $a(EXLCZ)991000000000358118 100 $a20060929d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSimulation /$fedited by Shane G. Henderson, Barry L. Nelson 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cElsevier$d2006 215 $a1 online resource (693 p.) 225 1 $aHandbooks in operations research and management science,$x0927-0507 ;$vv. 13 300 $aDescription based upon print version of record. 311 $a0-444-51428-7 320 $aIncludes bibliographical references and indexes. 327 $aFront cover; Title page; Copyright page; Dedication; Contents; Chapter 1. Stochastic Computer Simulation; 1. Scope of the Handbook; 2. Key concepts in stochastic simulation; 3. Organization of the Handbook; Acknowledgements; References; Chapter 2. Mathematics for Simulation; 1. Introduction; 2. Static simulation: Activity networks; 3. A model of ambulance operations; 4. Finite-horizon performance; 5. Steady-state simulation; Acknowledgements; Appendix. Proof of Proposition 15; References; Chapter 3. Uniform Random Number Generation; 1. Introduction; 2. Uniform random number generators 327 $a3. Linear recurrences modulo m4. Generators based on recurrences modulo 2; 5. Nonlinear RNGs; 6. Empirical statistical tests; 7. Conclusion, future work and open issues; Acknowledgements; References; Chapter 4. Nonuniform Random Variate Generation; 1. The main paradigms; 2. Uniformly bounded times; 3. Universal generators; 4. Indirect problems; 5. Random processes; 6. Markov chain methodology; Acknowledgements; References; Chapter 5. Multivariate Input Processes; 1. Introduction; 2. Constructing full joint distributions; 3. Parametric families of joint distributions 327 $a4. Constructing partially specified joint distributions5. Conclusion; References; Chapter 6. Arrival Processes, Random Lifetimes and Random Objects; 1. Arrival processes; 2. Generating random lifetimes; 3. Generating random objects; Acknowledgements; References; Chapter 7. Implementing Representations of Uncertainty; 1. Introduction; 2. Random-number generation; 3. Random-structure generation; 4. Application to variance reduction; 5. Conclusions and suggestions; References; Chapter 8. Statistical Estimation in Computer Simulation; 1. Introduction; 2. Background 327 $a3. Sample averages and time averages4. Stationary processes; 5. Analyzing data from independent replications; 6. Density estimation; 7. Summary; Acknowledgements; References; Chapter 9. Subjective Probability and Bayesian Methodology; Introduction; 1. Main concepts; 2. Computational issues; 3. Input distribution and model selection; 4. Joint input-output models; 5. Ranking and selection; 6. Discussion and future directions; Acknowledgement; References; Chapter 10. A Hilbert Space Approach to Variance Reduction; 1. Introduction; 2. Problem formulation and basic results; 3. Hilbert spaces 327 $a4. A Hilbert space approach to control variates5. Conditional Monte Carlo in Hilbert space; 6. Control variates and conditional Monte Carlo from a Hilbert space perspective; 7. Weighted Monte Carlo; 8. Stratification techniques; 9. Latin hypercube sampling; 10. A numerical example; 11. Conclusions; Acknowledgements; References; Chapter 11. Rare-Event Simulation Techniques: An Introduction and Recent Advances; 1. Introduction; 2. Rare-event simulation and importance sampling; 3. Rare-event simulation in a Markovian framework; 4. Large deviations of multidimensional random walks 327 $a5. Adaptive importance sampling techniques 330 $aThis Handbook is a collection of chapters on key issues in the design and analysis of computer simulation experiments on models of stochastic systems. The chapters are tightly focused and written by experts in each area. For the purpose of this volume "simulation? refers to the analysis of stochastic processes through the generation of sample paths (realization) of the processes. Attention focuses on design and analysis issues and the goal of this volume is to survey the concepts, principles, tools and techniques that underlie the theory and practice of stochastic simulation design and 410 0$aHandbooks in operations research and management science ;$vv. 13. 606 $aComputer simulation 606 $aStochastic systems 606 $aManagement$xSimulation methods 615 0$aComputer simulation. 615 0$aStochastic systems. 615 0$aManagement$xSimulation methods. 676 $a658.4034 676 $a003 701 $aHenderson$b Shane G$0887558 701 $aNelson$b Barry L$0511773 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583359203321 996 $aSimulation$91982704 997 $aUNINA