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A Guide to Simulation

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); • ra...

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Bibliographic Details
Main Authors: Bratley, Paul, Fox, Bennet L. (Author), Schrage, Linus E. (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 1987, 1987
Edition:2nd ed. 1987
Subjects:
Computer Simulation
Computer Modelling
Computational Mathematics And Numerical Analysis
Mathematics / Data Processing
Quantitative Economics
Econometrics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 5.3. Continuous Distributions
  • 5.4. Discrete 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
  • 6.4. Choosing a Good Generator Based on Theoretical Considerations
  • 6.5. Implementation of Uniform Random Number Generators
  • 6.6. Empirical Testing of Uniform Random Number Generators.-6.7. Proper Use of a Uniform Random Number Generator
  • 6.8. Exploiting Special Features of Uniform Generators
  • 1 Introduction
  • 1.1. Systems, Models, and Simulation
  • 1.2. Verification, Approximation, and Validation
  • 1.3. States, Events, and Clocks
  • 1.4. Simulation—Types and Examples
  • 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
  • 2.2. 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
  • 3.2. Analysis of Finite-Horizon Performance
  • 3.3. Analysis of Steady-State Performance
  • 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
  • 4 Rational Choice of Input Distributions
  • 4.1. Addition and the Normal Distribution
  • 7 Simulation Programming
  • 7.1. Simulation With General-Purpose Languages
  • 7.2. Simscript
  • 7.3. GPSS
  • 7.4. Simula
  • 7.5. General Considerations in Simulation Programming
  • 8 Programming to Reduce the Variance
  • 8.1. Choosing an Input Distribution
  • 8.2. Common Random Numbers
  • 8.3. Antithetic Variates
  • 8.4. Control Variates
  • 8.5. Stratified Sampling
  • 8.6. Importance Sampling
  • 8.7. Conditional Monte Carlo
  • 8.8. Summary
  • Appendix A The Shapiro—Wilk Test for Normality
  • Appendix L Routines for Random Number Generation
  • Appendix X Examples of Simulation Programming
  • References
  • Author Index

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