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Computer Science
System Modeling
System Modeling and Simulation
1. Introduction to System Modeling and Simulation
2. System Theory Fundamentals
3. Model Theory and Concepts
4. Simulation Fundamentals
5. The Simulation Study Methodology
6. Discrete-Event Simulation
7. Continuous Simulation
8. System Dynamics
9. Agent-Based Modeling
10. Monte Carlo Simulation
11. Hybrid and Multi-Paradigm Simulation
12. Input Modeling and Data Analysis
13. Random Number and Variate Generation
14. Model Verification and Validation
15. Output Analysis and Statistical Methods
16. Experimental Design for Simulation
17. Simulation Optimization
18. Advanced Simulation Topics
19. Simulation Software and Tools
20. Visualization and Animation
21. Applications and Case Studies
22. Professional Practice and Ethics
13.
Random Number and Variate Generation
13.1.
Random Number Theory
13.1.1.
Pseudo-Random vs True Random
13.1.2.
Uniformity Properties
13.1.3.
Independence Properties
13.1.4.
Periodicity and Cycle Length
13.2.
Random Number Generators
13.2.1.
Linear Congruential Generators
13.2.2.
Multiple Recursive Generators
13.2.3.
Tausworthe Generators
13.2.4.
Mersenne Twister
13.2.5.
Combined Generators
13.3.
Testing Random Number Quality
13.3.1.
Statistical Tests
13.3.2.
Empirical Tests
13.3.3.
Theoretical Tests
13.3.4.
Battery of Tests
13.4.
Random Variate Generation Techniques
13.4.1.
Inverse Transform Method
13.4.2.
Acceptance-Rejection Method
13.4.3.
Composition Method
13.4.4.
Convolution Method
13.4.5.
Special-Purpose Methods
13.5.
Generating Specific Distributions
13.5.1.
Uniform Distribution
13.5.2.
Exponential Distribution
13.5.3.
Normal Distribution
13.5.4.
Gamma Distribution
13.5.5.
Beta Distribution
13.5.6.
Discrete Distributions
13.6.
Multivariate Random Variate Generation
13.6.1.
Multivariate Normal
13.6.2.
Copula-Based Methods
13.6.3.
Transformation Methods
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12. Input Modeling and Data Analysis
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14. Model Verification and Validation