Generate Gaussian Random Numbers

Introduction

In this lab, we will explore how to generate Gaussian random numbers using the Box-Muller transform in JavaScript. The purpose of this lab is to provide a hands-on experience in understanding and implementing a commonly used technique for generating normally distributed random numbers. By the end of this lab, you will have a better understanding of how to generate Gaussian random numbers and how they can be used in various applications.

Skills Graph

Generating Gaussian Random Numbers using Box-Muller Transform

To generate Gaussian (normally distributed) random numbers using Box-Muller transform, follow these steps:

1. Open the Terminal/SSH and type node to start practicing coding.
2. Use the provided code snippet that utilizes the Box-Muller transform to generate random numbers with a Gaussian distribution.
3. The randomGauss() function provided in the code snippet generates a random number with a Gaussian distribution.
4. The output of randomGauss() function is a number between 0 and 1.
5. The output can be used for various applications, such as statistical simulations, data analysis, and machine learning.

const randomGauss = () => {
  const theta = 2 * Math.PI * Math.random();
  const rho = Math.sqrt(-2 * Math.log(1 - Math.random()));
  return (rho * Math.cos(theta)) / 10.0 + 0.5;
};

Example Usage:

randomGauss(); // 0.5

Summary

Congratulations! You have completed the Generate Gaussian Random Numbers lab. You can practice more labs in LabEx to improve your skills.