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Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.
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.

"1142779296"
Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.
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.

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Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

by Giuseppe Ciaburro
Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process
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Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process

by Giuseppe Ciaburro

Overview

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.
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.

Product Details

ISBN-13: 9781804614464
Publisher: Packt Publishing
Publication date: 11/30/2022
Sold by: Barnes & Noble
Format: eBook
Pages: 460
Sales rank: 743,452
File size: 16 MB
Note: This product may take a few minutes to download.

About the Author

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees holds a master's degree in chemical engineering from Universita degli Studi di Napoli Federico II, and a master's degreeand in acoustic and noise control from Seconda Universita degli Studi di Napoli. He works at the Built Environment Control Laboratory - Universita degli Studi della Campania "Luigi Vanvitelli".He has over 15 20 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer ITC courses (about 15 20 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching machine learning applications in acoustics and noise control. He was recently included in the world's top 2% scientists list by Stanford University.

Table of Contents

Table of Contents
  1. Introducing simulation models
  2. Understanding Randomness and Random Numbers
  3. Probability and Data Generating Process
  4. Working with Monte Carlo Simulations
  5. Simulation-Based Markov Decision Process
  6. Resampling methods
  7. Improving and optimizing systems
  8. Introducing evolutionary systems
  9. Simulation models for Financial Engineering
  10. Simulating Physical Phenomena by Neural Networks
  11. Modeling and Simulation for Project Management
  12. Simulation Model for Fault Diagnosis in dynamic system
  13. What is next?
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