Hands-On Simulation modeling with Python.

Section 1: Getting Started with Numerical Simulation
-Chapter 1: Introducing Simulation Models
-Chapter 2: Understanding Randomness and Random Numbers
-Chapter 3: Probability and Data Generation Processes
--Section 2: Simulation Modeling Algorithms and Techniques
-Chapter 4: Exploring Monte Carlo Simulations
-Chapter 5: Simulation-Based Markov Decision Processes
-Chapter 6: Resampling Methods
-Chapter 7: Using Simulation to Improve and Optimize Systems
--Section 3: Real-World Applications
-Chapter 8: Using Simulation Models for Financial Engineering
-Chapter 9: Simulating Physical Phenomena Using Neural Networks
-Chapter 10: Modeling and Simulation for Project Management
-Chapter 11: What's Next?
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Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.