Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

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 or 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 or 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...