Gaussian Distribution
The Gaussian or normal distribution refers to a probability distribution that looks like a bell curve, symmetric about the mean.
It is characterized by two parameters  mean and standard deviation.
Based on the central limit theorem, the normal distribution occurs frequently in statistics as the distribution of sample means.
It provides a good model for quantities that cluster around a central value, like IQ scores or sample averages.
Solution
Here is code to generate normally distributed random numbers:
Java


C++


Python


Normal distribution provides a good model for many natural phenomena.
Description: Gaussian Distribution
Gaussian distribution, also known as the normal distribution, is a probability distribution that describes how the values of a variable are distributed. It is a bellshaped curve characterized by its mean (μ) and standard deviation (σ). The mean defines the peak point, and the standard deviation defines the width of the “bell.” In a Gaussian distribution, about 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.
Solution
Here are implementations to generate Gaussiandistributed random numbers in Java, C++, and Python.
Java
In Java, you can use the Random
class to generate Gaussiandistributed numbers.


C++
C++ has a <random>
library that can be used to generate Gaussiandistributed numbers.


Python
In Python, the random
module in the standard library can be used, but a more commonly used library for this is numpy
.


Key Takeaways
 Gaussian distribution is widely used in statistics and data science.
 Defined by its mean and standard deviation, the distribution shows how much individual data points deviate from the mean.
 Java, C++, and Python offer builtin libraries for generating Gaussiandistributed random numbers.
 Understanding Gaussian distribution is essential for many applications, including machine learning and natural language processing.