TY -
GEN
AU - Mihram, G. Arthur
TI -
Simulation: statistical foundations and methodology
PB - Academic Press
SN - 9780080956015
SN - 0080956017
SN - 9780124959507
KW - Simulation, Méthodes de
KW - Processus stochastiques
KW - Simulation methods
KW - Stochastic processes
KW - Models, Theoretical
KW - MATHEMATICS ; General
KW - Electronic books
KW - Simulation
PY - 2010
N2 - Includes bibliographical references (pages 513-520). - Print version record
N2 - Print version record
N2 - 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
N2 - 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
N2 - 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
N2 - 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
N2 - 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
N2 - 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
N2 - 10.7 Comparison of Time Series from Simular Encounters
N2 - 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
BT - Mathematics in science and engineering ; v. 92
CY - New York
UR - http://slubdd.de/katalog?TN_libero_mab2
ER -