Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

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.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Summarizing simulation modeling concepts

Useful in cases where it is not possible to develop a mathematical model capable of effectively representing a phenomenon, simulation models imitate the operations performed over time by a real process. The simulation process involves generating an artificial history of the system to be analyzed; subsequently, the observation of this artificial history is used to trace information regarding the operating characteristics of the system itself and make decisions based on it.

The use of simulation models as a tool to aid decision-making processes has ancient roots and is widespread in various fields. Simulation models are used to study the behavior of a system over time, and are built based on a set of assumptions made on the behavior of the system that's expressed using mathematical-logical-symbolic relationships. These relationships are between the various entities that make up the system. The purpose of a model is to simulate changes...