#### 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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# Introducing Monte Carlo simulation

In simulation procedures, the evolution of a process is followed, but at the same time, forecasts of possible future scenarios are made. A simulation process consists of building a model that closely imitates a system. From the model, numerous samples of possible cases are generated and subsequently studied over time. After this, the results are analyzed over time, all while highlighting the alternative decisions that can be made.

The term Monte Carlo simulation was born at the beginning of the Second World War by J. von Neumann and S. Ulam as part of the Manhattan project at the Los Alamos nuclear research center. They replaced the parameters of the equations that describe the dynamics of nuclear explosions with a set of random numbers. The choice of the name Monte Carlo was due to the uncertainty of the winnings that characterize the famous casino of the Principality of Monaco.

## Monte Carlo components

To obtain a simulation with satisfactory...