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Hands-On Simulation Modeling with Python

You're reading from   Hands-On Simulation Modeling with Python Develop simulation models to get accurate results and enhance decision-making processes

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838985097
Length 346 pages
Edition 1st 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 (16) Chapters Close

Preface 1. Section 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. Section 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. Section 3: Real-World Applications
11. Chapter 8: Using Simulation Models for Financial Engineering 12. Chapter 9: Simulating Physical Phenomena Using Neural Networks 13. Chapter 10: Modeling and Simulation for Project Management 14. Chapter 11: What's Next? 15. Other Books You May Enjoy

Random number generation using Python

So far, we have seen what methods can be used for generating random numbers. We have also proposed some solutions in Python code for the generation of random numbers through some universally used methods. These applications have been useful for understanding the basis on which random number generators have been made. In Python, there is a specific module for the generation of random numbers: this is the random module. Let's examine what it is.

Introducing the random module

The random module implements PRNGs for various distributions. The random module is based on the Mersenne Twister algorithm, which was originally developed to produce inputs for Monte Carlo simulations. The Mersenne Twister algorithm is a PRNG that produces almost uniform numbers suitable for a wide range of applications.

It is important to note that random numbers are generated using repeatable and predictable deterministic algorithms. They begin with a certain...

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