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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

Introducing the Monte Carlo simulation

In simulation procedures, the evolution of a process is followed, but at the same time, forecasts of possible future scenarios are made. A simulation process consists of building a model that closely imitates a system. From the model, numerous samples of possible cases are generated and subsequently studied over time. After this, the results are analyzed over time, all while highlighting the alternative decisions that can be made.

The term Monte Carlo simulation was born at the beginning of the Second World War by J. von Neumann and S. Ulam as part of the Manhattan project at the Los Alamos nuclear research center. They replaced the parameters of the equations that describe the dynamics of nuclear explosions with a set of random numbers. The choice of the name Monte Carlo was due to the uncertainty of the winnings that characterize the famous casino of the Principality of Monaco.

Monte Carlo components

To obtain a simulation with satisfactory...

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