#### Overview of this book

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.
Preface
Section 1: Getting Started with Numerical Simulation
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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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# Summary

In this chapter, we summarized the technologies that we have exposed throughout this book. We have seen how to generate random numbers and listed the most frequently used algorithms for generating pseudo-random numbers. Then, we saw how to apply Monte Carlo methods for numerical simulation based on the assumptions of two fundamental laws: the law of large numbers and the central limit theorem. We then went on to summarize the concepts that Markovian models are based on and then analyzed the various resampling methods that are available. After that, we explored the most used numerical optimization techniques and learned how to use ANNs for numerical simulation.

Subsequently, we mentioned a series of fields in which numerical simulation is widely used and looked at the next steps that will allow simulation models to evolve.

In this book, we studied various computational statistical simulations using Python. We started with the basics in order to understand various methods...