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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
10.
Monte Carlo Simulation
10.1.
Basic Principles
10.1.1.
Random Sampling Concepts
10.1.2.
Law of Large Numbers
10.1.3.
Central Limit Theorem
10.1.4.
Convergence Properties
10.2.
Static Monte Carlo Methods
10.2.1.
Basic Sampling
10.2.2.
Importance Sampling
10.2.3.
Stratified Sampling
10.2.4.
Latin Hypercube Sampling
10.3.
Dynamic Monte Carlo Methods
10.3.1.
Markov Chain Monte Carlo
10.3.2.
Sequential Monte Carlo
10.3.3.
Particle Filters
10.4.
Variance Reduction Techniques
10.4.1.
Antithetic Variates
10.4.2.
Control Variates
10.4.3.
Importance Sampling
10.4.4.
Stratified Sampling
10.5.
Applications
10.5.1.
Risk Analysis
10.5.2.
Financial Modeling
10.5.3.
Engineering Reliability
10.5.4.
Physics Simulations
10.5.5.
Optimization Problems
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9. Agent-Based Modeling
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11. Hybrid and Multi-Paradigm Simulation