# Folded normal distribution

The folded normal distribution is a probability distribution related to the normal distribution. Given a normally distributed random variable X with mean μ and variance σ2, the random variable Y = |X| has a folded normal distribution. Such a case may be encountered if only the magnitude of some variable is recorded, but not its sign. The distribution is called Folded because probability mass to the left of the x = 0 is "folded" over by taking the absolute value.

The cumulative distribution function (CDF) is given by

$F_{Y}(y;\mu ,\sigma )=\int _{0}^{y}{\frac {1}{\sigma {\sqrt {2\pi }}}}\,\exp \left(-{\frac {(-x-\mu )^{2}}{2\sigma ^{2}}}\right)\,dx+\int _{0}^{y}{\frac {1}{\sigma {\sqrt {2\pi }}}}\,\exp \left(-{\frac {(x-\mu )^{2}}{2\sigma ^{2}}}\right)\,dx.$ Using the change-of-variables z = (x − μ)/σ, the CDF can be written as

$F_{Y}(y;\mu ,\sigma )=\int _{-\mu /\sigma }^{(y-\mu )/\sigma }{\frac {1}{\sqrt {2\pi }}}\,\exp \left(-{\frac {1}{2}}\left(z+{\frac {2\mu }{\sigma }}\right)^{2}\right)dz+\int _{-\mu /\sigma }^{(y-\mu )/\sigma }{\frac {1}{\sqrt {2\pi }}}\,\exp \left(-{\frac {z^{2}}{2}}\right)dz.$ The expectation is then given by

$E(y)=\sigma {\sqrt {2/\pi }}\exp(-\mu ^{2}/2\sigma ^{2})+\mu \left[1-2\Phi (-\mu /\sigma )\right],$ where Φ(•) denotes the cumulative distribution function of a standard normal distribution.

The variance is given by

$\operatorname {Var} (y)=\mu ^{2}+\sigma ^{2}-\left\{\sigma {\sqrt {2/\pi }}\exp(-\mu ^{2}/2\sigma ^{2})+\mu \left[1-2\Phi (-\mu /\sigma )\right]\right\}^{2}.$ Both the mean, μ, and the variance, σ2, of X can be seen to location and scale parameters of the new distribution.

## Related distributions 