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


Knowing the basics of probability theory in depth helps us to understand how random phenomena work. We discovered the differences between a priori, compound, and conditioned probabilities. We have also seen how Bayes' theorem allows us to calculate the conditional probability of a cause of an event, starting from the knowledge of the a priori probabilities and the conditional probability. Next, we analyzed some probability distributions, and how such distributions can be generated in Python.

In the next chapter, we will learn the basic concepts of Monte Carlo simulation and explore some of its applications. Then, we will discover how to generate a sequence of numbers that have been randomly distributed according to a Gaussian. Finally, we will take a look at the practical application of the Monte Carlo method in order to calculate a definite integral.