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

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781838985097

Length 346 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Giuseppe Ciaburro

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Table of Contents (16) Chapters

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

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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...