This is the code repository for Hands-On Simulation Modeling with Python, published by Packt.
Develop simulation models to get accurate results and enhance decision-making processes
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.
This book covers the following exciting features: <First 5 What you'll learn points>
- Gain an overview of the different types of simulation models
- Get to grips with the concepts of randomness and data generation process
- Understand how to work with discrete and continuous distributions
- Work with Monte Carlo simulations to calculate a definite integral
- Find out how to simulate random walks using Markov chains
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import random
import statistics
import matplotlib.pyplot as plt
Following is what you need for this book: Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Python 3.6 and above | Windows, Mac OS X, and Linux |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book [http://www.packtpub.com/sites/default/files/downloads/9781838985097_ColorImages.pdf] (Graphics Bundle Link).
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Reinforcement Learning Algorithms with Python [Packt] [Amazon]
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Practical Data Analysis Using Jupyter Notebook [Packt] [Amazon]
Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master’s degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years’ 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
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