Changes and additions are sprinkled throughout. Among the significant new features are: - Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5); - gradient estimation (Sections 1. 6, 2. 5, and 4. 9); - better handling of asynchronous observations (Sections 3. 3 and 3. 6); - radically updated treatment of indirect estimation (Section 3. 3); - new section on standardized time series (Section 3. 8); - better way to generate random integers (Section 6. 7. 1) and fractions (Appendix L, program UNIFL); - thirty-seven new problems plus improvements of old problems. Helpful comments by Peter Glynn, Barry Nelson, Lee Schruben, and Pierre Trudeau stimulated several changes. Our new random integer routine extends ideas of Aarni Perko. Our new random fraction routine implements Pierre L'Ecuyer's recommended composite generator and provides seeds to produce disjoint streams. We thank Springer-Verlag and its late editor, Walter Kaufmann-Bilhler, for inviting us to update the book for its second edition. Working with them has been a pleasure. Denise St-Michel again contributed invaluable text-editing assistance. Preface to the First Edition Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs. It is widely applied in engineering, in business, and in the physical and social sciences.

Inhaltsangabe1 Introduction.- 1.1. Systems, Models, and Simulation.- 1.2. Verification, Approximation, and Validation.- 1.2.1. Verifying a Program.- 1.2.2. Approximation and Validation.- 1.3. States, Events, and Clocks.- 1.4. Simulation-Types and Examples.- 1.4.1. Synchronous and Asynchronous Discrete-Event Simulation.- 1.4.2. Continuous Simulation.- 1.4.3. Hybrid Simulation.- 1.5. Introduction to Random Numbers.- 1.6. Perspective on Experimental Design and Estimation.- 1.7. Clock Mechanisms.- 1.8. Hints for Simulation Programming.- 1.9. Miscellaneous Problems.- 2 Variance Reduction.- 2.1. Common Random Numbers.- 2.1.1. Informal Approach.- 2.1.2. Formal Development.- 2.1.3. Auxiliary Results.- 2.2. Antithetic Variates.- 2.3. Control Variates.- 2.4. Stratification.- 2.5. Importance Sampling.- 2.6. Conditional Monte Carlo.- 2.7. Jackknifing.- 3 Output Analysis.- 3.1. Introduction.- 3.1.1. Finite-Horizon Versus Steady-State Performance.- 3.1.2. Fixed Sample Size Versus Sequential Sampling.- 3.2. Analysis of Finite-Horizon Performance.- 3.2.1. Absolute Performance Estimation.- 3.2.2. Relative Performance Estimation.- 3.3. Analysis of Steady-State Performance.- 3.3.1. Batch Means.- 3.3.2. Regenerative Methods.- 3.3.3. Spectral Analysis Methods.- 3.3.4. Autoregressive Methods.- 3.3.5. Recommendations.- 3.4. Analysis of Transaction-Based Performance.- 3.5. Indirect Estimation via r = ?s.- 3.6. Problems.- 3.7. Renewal Theory Primer.- 3.8. Standardized Time Series.- 3.8.1. Steady State.- 3.8.2. Transients.- 4 Rational Choice of Input Distributions.- 4.1. Addition and the Normal Distribution.- 4.2. Multiplication and the Lognormal.- 4.3. Memorylessness and the Exponential.- 4.4. Superposition, the Poisson, and the Exponential.- 4.5. Minimization and the Weibull Distribution.- 4.6. A Mixed Empirical and Exponential Distribution.- 4.7. Extreme Values and Spacings.- 4.8. When Not to Use a Theoretical Distribution.- 4.9. Nonstationary Poisson Processes.- 5 Nonuniform Random Numbers.- 5.1. Introduction.- 5.2. General Methods.- 5.2.1. Inversion.- 5.2.2. Tabular Approximation of the Inverse Transform.- 5.2.3. Empirical cdf's.- 5.2.4. A Mixed Empirical and Exponential Distribution.- 5.2.5. Rejection.- 5.2.6. Generalized Rejection.- 5.2.7. Composition.- 5.2.8. The Alias Method for Discrete Distributions.- 5.2.9. Functional Approximations of the Inverse Transform.- 5.2.10. Other Ingenious Methods.- 5.3. Continuous Distributions.- 5.3.1. The Nonstandard Normal Distribution.- 5.3.2. The Multivariate (Dependent) Normal Distribution.- 5.3.3. Symmetric Stable Variates.- 5.3.4. The Cauchy Distribution.- 5.3.5. The Lognormal Distribution.- 5.3.6. The Exponential Distribution.- 5.3.7. The Hyperexponential Distribution.- 5.3.8. The Laplace and Exponential Power Distributions.- 5.3.9. Erlang and Gamma Distributions.- 5.3.10. The Beta Distribution.- 5.3.11. The Chi-square Distribution.- 5.3.12. The F-Distribution.- 5.3.13. The t-Distribution.- 5.3.14. The Weibull Distribution.- 5.3.15. The Gumbel Distribution.- 5.3.16. The Logistic Distribution.- 5.3.17. The Generalized Lambda Distribution.- 5.3.18. Nonhomogeneous Poisson Processes.- 5.4. Discrete Distributions.- 5.4.1. The Binomial Distribution.- 5.4.2. The Poisson Distribution.- 5.4.3. Compound Poisson Distributions.- 5.4.4. The Hypergeometric Distribution.- 5.4.5. The Geometric Distribution.- 5.4.6. The Negative Binomial and Pascal Distributions.- 5.4.7. Multidimensional Poisson Distributions.- 5.5. Problems.- 5.6. Timings.- 6 Uniform Random Numbers.- 6.1. Random Introductory Remarks.- 6.2. What Constitutes Randomness.- 6.3. Classes of Generators.- 6.3.1. Random Devices.- 6.3.2. Tables.- 6.3.3. The Midsquare Method.- 6.3.4. Fibonacci and Additive Congruential Generators.- 6.3.5. Linear Congruential Generators.- 6.3.6. Linear Recursion mod 2 Generators.- 6.3.7. Combinations of Generators.- 6.4. Choosing a Good Generator Based on Theoretical Considerations.- 6.4.1. Serial Correlation of Linear Congruential Generators.-

Hersteller:
Springer Verlag GmbH
juergen.hartmann@springer.com
Tiergartenstr. 17
DE 69121 Heidelberg