Simulation / / edited by Shane G. Henderson, Barry L. Nelson

• Record Nr.: UNINA9910583359203321
• Pubbl/distr/stampa: Amsterdam ; ; Boston, : Elsevier, 2006
• ISBN: 1-280-63598-3; 9786610635986; 0-08-046476-9
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (693 p.)
• Collana: Handbooks in operations research and management science, , 0927-0507 ; ; v. 13
• Altri autori (Persone): HendersonShane G; NelsonBarry L
• Disciplina: 658.4034; 003
• Soggetti: Computer simulation; Stochastic systems; Management - Simulation methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: Front 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; 3. 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; 4. 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; 3. 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; 4. 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; 5. Adaptive importance sampling techniques
• Sommario/riassunto: This 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