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
Dedication
About Packt
Contributors
Preface
Index

Running Monte Carlo simulations


The Monte Carlo simulation (see the example, http://news.mit.edu/2010/exp-monte-carlo-0517 or https://en.wikipedia.org/wiki/Monte_Carlo_method) is one of the elementary computational techniques. In this recipe, we will explain how it can be implemented efficiently in Julia.

Getting ready

Consider the following problem. Assume that on each day, a random volume of water leaks from a pipe to a container. The amount of water that leaks out is greater than zero, but less than one. How many days do we need to wait till a container having a volume equal to one is filled, if on each day the amount of water that leaks is a uniformly random value between zero and one?

Formally, we repeatedly draw independent random numbers from a uniform distribution on the 

interval. How many draws, on average, are required until the sum of drawn numbers is greater than or equal to 1? Let

 be a sequence of independent random variables. We want to find the following:

We will approximate...