In probability theory and directional statistics, a wrapped normal distribution is a wrapped probability distribution that results from the "wrapping" of the normal distribution around the unit circle. It finds application in the theory of Brownian motion and is a solution to the heat equation for periodic boundary conditions. It is closely approximated by the von Mises distribution, which, due to its mathematical simplicity and tractability, is the most commonly used distribution in directional statistics.

The probability density function of the wrapped normal distribution is

where μ and σ are the mean and standard deviation of the unwrapped distribution, respectively. Expressing the above density function in terms of the characteristic function of the normal distribution yields:

where ${\displaystyle \vartheta (\theta ,\tau )}$ is the Jacobi theta function, given by

The wrapped normal distribution may also be expressed in terms of the Jacobi triple product:

where ${\displaystyle z=e^{i(\theta -\mu )}\,}$ and ${\displaystyle q=e^{-\sigma ^{2}}.}$

In terms of the circular variable ${\displaystyle z=e^{i\theta }}$ the circular moments of the wrapped normal distribution are the characteristic function of the normal distribution evaluated at integer arguments:

where ${\displaystyle \Gamma }$ is some interval of length ${\displaystyle 2\pi }$. The first moment is then the average value of z, also known as the mean resultant, or mean resultant vector: