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Medientyp:
E-Book
Titel:
Simulation: statistical foundations and methodology
Enthält:
Front Cover; Simulation: Statistical Foundations and Methodology; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1. Models; 1.1 Prolegomenon; 1.2 Categories of Models; 1.3 A Cross Classification; 1.4 Stochasticity and Randomness in Systems; 1.5 The Uncertainty Principle of Modeling; 1.6 Summary; Chapter 2. The Stochastic Variate; 2.1 Introductory Remarks; 2.2 The Binomial Distribution; 2.3 The Geometric Distribution; 2.4 Parameters; 2.5 Continuous Random Variables; 2.6 Transformations of Random Variables; 2.7 The CDF Transformation
2.8 Generation of Uniformly Distributed Random Variables2.9 Testing Random Number Generators; 2.10 Moments of Random Variables; 2.11 Bivariate Distributions; 2.12 The Central Limit Theorem; 2.13 Other Techniques for Generating Random Variables; 2.14 The Bivariate Normal Distribution; 2.15 The Multivariate Normal Distribution; Chapter 3. Time Series; 3.1 Introduction; 3.2 Some Exemplary Time Series; 3.3 Generation of Autocorrelated Random Variables; 3.4 The Poisson Process; 3.5 The Spectrum; 3.6 Summary; Chapter 4. The Monte Carlo Method; 4.1 Experiments with Random Numbers
4.2 Buffon's Needle Problem4.3 Numerical Integration Techniques; 4.4 The Law of Large Numbers; 4.5 Random Walks; 4.6 Accuracy of Monte Carlo Estimates; 4.7 Queues; 4.8 Monte Carlo versus Simulation; Chapter 5. Modeling; 5.1 Stages of Model Development; 5.2 Elements of Systems Analysis; 5.3 Elements of System Synthesis; 5.4 Ad Hoc Simulation Programming Languages; 5.5 Model Verification; 5.6 Model Validation; 5.7 Model Analysis; 5.8 A Scholium on Modeling; Chapter 6. Fundamentals of Simular Experimentation; 6.1 Introduction; 6.2 Statistical Analyses
6.3 Distributional Forms Attainable by Simular Responses6.4 Estimation of Parameters in Simular Response Distributions; 6.5 Statistical Comparisons; Chapter 7. Experimental Designs for Qualitative Factors; 7.1 Introductory Remarks; 7.2 The Factorial Design; 7.3 The 22 Factorial Design; 7.4 The 23 Factorial Design; 7.5 Generalized Interactions and Partial Replicates; 7 6 Factors at Three Levels; 7.7 The 32 Factorial Design; 7.8 Other Factorial Designs; Chapter 8. Experimental Designs for Quantitative Factors; 8.1 Introduction; 8.2 Experimentation with a Single Quantitative Factor
8.3 Multiple Regression: The General Linear Model8.4 The Multivariate Simular Response; 8.5 A Scholium on Simular Experimentation; Chapter 9. The Search for Optimal System Conditions; 9.1 Introduction; 9.2 The Response Surface; 9.3 The Search for the Optimal Response; 9.4 The Intensive Search; 9.5 Summary: A Guide to Response Surface Methodology; Chapter 10. The Simulation as a Stochastic Process; 10.1 Introduction; 10.2 Stationarity and Ergodicity; 10.3 Removal of Trends; 10.4 The Correlogram; 10.5 Spectral Analysis; 10.6 Tests for Random Number Chains
10.7 Comparison of Time Series from Simular Encounters
Hersteller der Reproduktion:
[S.l.]: HathiTrust Digital Library
Reproduktionsnotiz:
Online-Ausg. [S.l.] : HathiTrust Digital Library
Entstehung:
Anmerkungen:
Includes bibliographical references (pages 513-520). - Print version record
Print version record
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002
Beschreibung:
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank