Gaussian Distribution

Definition of Gaussian Distribution

Gaussian Distribution – A Gaussian or normal distribution is a type of probability distribution that is bell-shaped and symmetrical. This distribution is often used in statistics to model real-world data.

What is a Gaussian Distribution used for?

A Gaussian Distribution, more commonly known as a normal distribution or bell curve, is a probability distribution that is used to describe the distribution of data points around a central value. It is based on the idea that when data points are plotted in a graph and measured by their relative frequency, they form a symmetric bell-shaped curve. The shape of this curve is determined by its mean (the average of all values) and its standard deviation (the amount by which each value deviates from the mean). In other words, if the mean and standard deviation can be calculated, then it is possible to estimate how many data points will fall within any range along the normal curve.

This probability distribution has numerous applications in science, engineering, economics and statistics. The most common use of the Gaussian Distribution is in predictive analytics and machine learning algorithms where it can be used to measure patterns in data sets and predict future trends. It is also used to calculate probabilities for various sampling methods such as predicting how likely it is that two samples come from the same population or calculating expected outcomes given certain input parameters. Additionally, it can be applied to determine confidence intervals for estimates derived from sample data sets. Finally, it can also be used for optimization problems where decision makers need to select “optimal” solutions among multiple alternatives.

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