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

Random number simulation

The availability of random numbers is a necessary requirement in many applications. In some cases, the quality of the final application strictly depends on the possibility of generating good quality random numbers. Think, for example, of applications such as video games, cryptography, generating visuals or sound effects, telecommunications, signal processing, optimizations, and simulations. In an algorithm of this type, decisions are made based on the pull of a virtual currency, which is based on a randomly chosen value.

There is no single or general definition of a random number since it often depends on the context. The concept of random number itself is not absolute, as any number or sequence of numbers can appear to be random to an observer, but not to another who knows the law with which they are generated. Put simply, a random number is defined as a number selected in a random process from a finite set of numbers. With this definition, we focus on...