Preface

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 learn about various computational statistical simulations using Python.

Starting with the fundamentals of simulation modeling, you’ll learn about 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 the Markov Decision Process, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you make progress, 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 through 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 be able to construct and deploy simulation models of your own to solve real-world challenges.