Definition:Normal Distribution

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Definition

Normal-distribution-with-sigmas.png

Let $X$ be a continuous random variable on a probability space $\struct {\Omega, \Sigma, \Pr}$.


Then $X$ has a normal distribution if and only if the probability density function of $X$ is:

$\map {f_X} x = \dfrac 1 {\sigma \sqrt {2 \pi} } \map \exp {-\dfrac {\paren {x - \mu}^2} {2 \sigma^2} }$

for $\mu \in \R, \sigma \in \R_{> 0}$.


This is written:

$X \sim \Gaussian \mu {\sigma^2}$


Also known as

The normal distribution is also commonly known as the Gaussian distribution, for Carl Friedrich Gauss.

Contrary to the usual house style, the former term is preferred on $\mathsf{Pr} \infty \mathsf{fWiki}$.

A popular term for the normal distribution is bell-shaped, from the shape of the graph of its frequency function.


Also see

  • Results about the normal distribution can be found here.


Technical Note

The $\LaTeX$ code for \(\Gaussian {\mu} {\sigma^2}\) is \Gaussian {\mu} {\sigma^2} .

When either argument is a single character, it is usual to omit the braces:

\Gaussian \mu {\sigma^2}


Sources