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Hands-On Simulation Modeling with Python

You're reading from   Hands-On Simulation Modeling with Python Develop simulation models for improved efficiency and precision in the decision-making process

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804616888
Length 460 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with Numerical Simulation
2. Chapter 1: Introducing Simulation Models FREE CHAPTER 3. Chapter 2: Understanding Randomness and Random Numbers 4. Chapter 3: Probability and Data Generation Processes 5. Part 2:Simulation Modeling Algorithms and Techniques
6. Chapter 4: Exploring Monte Carlo Simulations 7. Chapter 5: Simulation-Based Markov Decision Processes 8. Chapter 6: Resampling Methods 9. Chapter 7: Using Simulation to Improve and Optimize Systems 10. Chapter 8: Introducing Evolutionary Systems 11. Part 3:Simulation Applications to Solve Real-World Problems
12. Chapter 9: Using Simulation Models for Financial Engineering 13. Chapter 10: Simulating Physical Phenomena Using Neural Networks 14. Chapter 11: Modeling and Simulation for Project Management 15. Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems 16. Chapter 13: What’s Next? 17. Index 18. Other Books You May Enjoy

Summary

In this chapter, we summarized the technologies that we have exposed throughout this book. We have seen how to generate random numbers and have listed the most frequently used algorithms used to generate pseudo-random numbers. Then, we saw how to apply the Monte Carlo methods for numerical simulation based on the assumptions of two fundamental laws: the law of large numbers and the central limit theorem. We then went on to summarize the concepts that Markovian models are based on and then analyzed the various available resampling methods. After that, we explored the most used numerical optimization techniques and learned how to use artificial neural networks for numerical simulation.

Subsequently, we mentioned a series of fields in which numerical simulation is widely used and then looked at the next steps that will allow simulation models to evolve.

In this book, we studied various computational statistical simulations using Python. We started with the basics to understand...

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