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

Introducing project management

To assess the consequences of a strategic or tactical move in advance, companies need reliable predictive systems. Predictive analysis systems are based on data collection and the projection of reliable scenarios in the medium- and long-term. In this way, we can provide indications and guidelines for complex strategies, especially those that must consider numerous factors from different entities.

This allows us to examine the results of the evaluation in a more complete and coordinated way since we can simultaneously consider a range of values and, consequently, a range of possible scenarios. Finally, when managing complex projects, the use of artificial intelligence to interpret data has increased, thus giving these projects meaning. This is because we can perform a sophisticated analysis of the information in order to improve the strategic decision-making process we will undertake. This methodology allows us to search and analyze data from different...