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Julia 1.0 Programming Cookbook

You're reading from   Julia 1.0 Programming Cookbook Over 100 numerical and distributed computing recipes for your daily data science work?ow

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788998369
Length 460 pages
Edition 1st Edition
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Authors (2):
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Przemysław Szufel Przemysław Szufel
Author Profile Icon Przemysław Szufel
Przemysław Szufel
Bogumił Kamiński Bogumił Kamiński
Author Profile Icon Bogumił Kamiński
Bogumił Kamiński
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Table of Contents (12) Chapters Close

Preface 1. Installing and Setting Up Julia 2. Data Structures and Algorithms FREE CHAPTER 3. Data Engineering in Julia 4. Numerical Computing with Julia 5. Variables, Types, and Functions 6. Metaprogramming and Advanced Typing 7. Handling Analytical Data 8. Julia Workflow 9. Data Science 10. Distributed Computing 11. Other Books You May Enjoy

Analyzing a queuing system


In the Running Monte Carlo simulations recipe, we presented basic methods for running a Monte Carlo simulation. Now, we will show you how you can calculate the confidence interval of a simulation output using bootstrapping.

Getting ready

First, you need to understand how an M/M/1 queue works. A basic introduction to this topic can be found at https://www.britannica.com/science/queuing-theory or https://en.wikipedia.org/wiki/M/M/1_queue. For our purposes, it is sufficient to know that in this model the time between two consecutive arrivals of a customer to the system has an exponential distribution (see http://mathworld.wolfram.com/ExponentialDistribution.html or https://en.wikipedia.org/wiki/Exponential_distribution). Customers are then served by a single server in a first-in/first-out schedule. The time taken by the service is also exponentially distributed.

Here is a simple visualization of a single server queue:

We are interested in the average time, over the long...

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