Get to grips with constructing state of the art simulation models with python.
Key Features
- Understand various statistical and physical simulations to improve systems using Python
- Learn to create a numerical prototype of a real model using hands-on examples
- Evaluate performance and output results based on how the prototype would work in the real environment
Book Description
This book is a comprehensive guide to understand various computational statistical simulations using Python.
This book will start with the required foundation to understand various methods and techniques to delve into complex topics. Developers working with simulation models will be able to put their knowledge to work with this practical guide. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.
Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by exploring the numerical simulation algorithms, including an overview of relevant applications. You'll learn how to use Python to develop simulation model and understand how to use the several Python packages. You will then explore various numerical simulation algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you will be able to construct simulation models.
What you will learn
- Get to grips with the concepts of randomness and data generation process
- Delve into Resampling methods
- Learn how to work with Monte Carlo Simulations
- Use simulation to improve or optimize systems
- Learn how to run efficient simulations to analyze real-world systems
- Learn to run efficient simulations to analyze real-world systems
Who This Book Is For
This book is for Data Scientists, simulation engineers, or anyone who is already familiar with the basic computational methods but now wants to implement various simulation techniques such as Monte-Carlo methods, statistical simulation using Python.
Table of Contents
- Introducing simulation models
- Understanding Randomness and Random Numbers
- Probability and Data Generating Process
- Working with Monte Carlo Simulations
- Simulation-Based Markov Decision Process
- Resampling methods
- Improving and optimizing systems
- Introducing evolutionary systems
- Simulation models for Financial Engineering
- Simulating Physical Phenomena by Neural Networks
- Modeling and Simulation for Project Management
- Simulation Model for Fault Diagnosis in dynamic system
- What is next?
Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory―Universitą degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of professional experience in programming (Python, R, and MATLAB), first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.
