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?

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Testing uniform distribution

Test adaptation (that is, the goodness of fit) in general, has the purpose of verifying whether a variable under examination does or does not have a certain hypothesized distribution on the basis, as usual, of experimental data. It is used to compare a set of frequencies observed in a sample, with similar theoretical quantities assumed for the population. By means of the test, it is possible to quantitatively measure the degree of deviation between the two sets of values.

The results obtained in the samples do not always exactly agree with the theoretical results that are expected according to the rules of probability. Indeed, it is very rare for this to occur. For example, although theoretical considerations lead us to expect 100 heads and 100 tails from 200 flips of a coin, it is rare that these results are obtained exactly. However, despite this, we must not unnecessarily deduce that the coin is rigged.