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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

The pseudorandom number generator

The generation of real random sequences using deterministic algorithms is impossible: at most, pseudorandom sequences can be generated. These are, apparently, random sequences that are actually perfectly predictable and can be repeated after a certain number of extractions. A pseudorandom number generator (PRNG) is an algorithm designed to output a sequence of values that appear to be generated randomly.

The pros and cons of a random number generator

A random number generation routine must have the following attributes:

  • Be replicable
  • Be fast
  • Not have large gaps between two generated numbers
  • Have a sufficiently long-running period
  • Be able to generate numbers with statistical properties that are as close as possible to ideal ones

The most common cons of random number generators are as follows:

  • Numbers not uniformly distributed
  • Discretization of the generated numbers
  • Incorrect mean or variance
  • ...
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