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


In this chapter, we learned how to resample a dataset. We analyzed several techniques that approach the problem differently. First, we analyzed the basic concepts of sampling and learned about the reasons that push us to use a sample extracted from a population. We then examined the pros and cons of this choice. We also analyzed how a resampling algorithm works.

We then tackled the first resampling method: the Jackknife method. We first defined the concepts behind the method and then moved on to the procedure, which allows us to obtain samples from the original population. To put the concepts we learned into practice, we applied Jackknife resampling to a practical case.

We then explored the bootstrap method, which builds unobserved but statistically, like the observed samples. This is accomplished by resampling the observed series through an extraction procedure where we reinsert the observations. After defining the method, we worked through an example to highlight the...