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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

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

View More author details

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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Technical requirements

In this chapter, we will introduce random number generation techniques. In order to understand these topics, a basic knowledge of algebra and mathematical modeling is needed.

To work with the Python code in this chapter, you need the following files (available on GitHub at https://github.com/PacktPublishing/Hands-On-Simulation-Modeling-with-Python):

LinearCongruentialGenerator.py
LearmouthLewisGenerator.py
LaggedFibonacciAlgorithm.py
UniformityTest.py
Random.Generation.py

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Authors (1)

Giuseppe Ciaburro

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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