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

Applying simulation model to real life

The algorithms that we have analyzed in detail throughout this book represent valid tools for simulating real systems. This is why they are widely used in real life to carry out research on the possible evolution of a phenomenon, following a possible choice made on it.

Let's look at some specific examples.

Modeling in healthcare

In the healthcare sector, simulation models have a significant weight and are widely used to simulate the behavior of a system in order to extract knowledge. For example, it is necessary to demonstrate the clinical efficacy of the health intervention under consideration before undertaking an economic analysis. The best available sources are randomized controlled trials. Trials, however, are often designed to leave out the economic aspects, so the key parameters for economic evaluations are generally absent. Therefore, a method is needed to evaluate the effect of disease progression, in order to limit the...