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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

Chapter 4: Exploring Monte Carlo Simulations

Monte Carlo simulation is used to reproduce and numerically solve a problem in which random variables are also involved, and whose solution by analytical methods is too complex or impossible. In addition, the use of simulation allows you to test the effects of changes in the input variables or in the output function more easily and with a high degree of detail. Starting from modeling the processes and generating random variables, simulations composed of multiple runs capable of obtaining an approximation of the probability of certain results are performed.

This method has assumed great importance in many scientific and engineering areas, above all for its ability to deal with complex problems that previously could only be solved through deterministic simplifications. It is mainly used in three distinct classes of problems: optimization, numerical integration, and the generation of probability functions. In this chapter, we will explore...

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