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Giuseppe Ciaburro - Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes

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Giuseppe Ciaburro Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes
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Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide

Key Features
  • Learn to create a digital prototype of a real model using hands-on examples
  • Evaluate the performance and output of your prototype using simulation modeling techniques
  • Understand various statistical and physical simulations to improve systems using Python
Book Description

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, youll understand various computational statistical simulations using Python.

Starting with the fundamentals of simulation modeling, youll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. Youll 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, youll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, youll 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, youll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.

What you will learn
  • 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
  • Obtain robust estimates of confidence intervals and standard errors of population parameters
  • Discover how to use optimization methods in real-life applications
  • Run efficient simulations to analyze real-world systems
Who this book is for

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.

Table of Contents
  1. Introducing Simulation Models
  2. Understanding Randomness and Random Numbers
  3. Probability and Data Generating Processes
  4. Exploring Monte Carlo Simulations
  5. Simulation-Based Markov Decision Process
  6. Resampling Methods
  7. Using Simulations to Improve and Optimize Systems
  8. Using Simulation Models for Financial Engineering
  9. Simulating Physical Phenomena Using Neural Networks
  10. Modeling and Simulation for Project Management
  11. Whats Next?

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Hands-On Simulation Modeling with Python

Develop simulation models to get accurate results and enhance decision-making processes

Giuseppe Ciaburro

BIRMINGHAMMUMBAI Hands-On Simulation Modeling with Python Copyright 2020 Packt - photo 2

BIRMINGHAMMUMBAI

Hands-On Simulation Modeling with Python

Copyright 2020 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor : Sunith Shetty

Acquisition Editor : Devika Battike

Senior Editor : David Sugarman

Content Development Editor : Joseph Sunil

Technical Editor : Sonam Pandey

Copy Editor : Safis Editing

Project Coordinator : Aishwarya Mohan

Proofreader : Safis Editing

Indexer : Manju Arasan

Production Designer : Joshua Misquitta

First published: July 2020

Production reference: 1160720

Published by Packt Publishing Ltd.

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ISBN 978-1-83898-509-7

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Contributors
About the author

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two masters 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 Universit 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.

About the reviewers

Greg Walters has been involved with computers and computer programming since 1972. He is well versed in Visual Basic, Visual Basic.NET, Python, and SQL, and is an accomplished user of MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous individuals in many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is currently retired and, in his spare time, is a musician and loves to cook. He is also open to working as a freelancer on various projects.

Yoon Hyup Hwang is a seasoned data scientist in the marketing and finance industries and is the author of two applied machine learning books. He has almost a decade of experience building numerous machine learning models and data science products. He holds an M.S.E. in Computer and Information Technology from the University of Pennsylvania and a B.A. in Economics from the University of Chicago. He enjoys training various martial arts, snowboarding, and roasting coffee. He currently lives in the Greater New York Area with his artist wife, Sunyoung, and a playful dog, Dali (named after Salvador Dali).

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