Rectified Gaussian distribution should not be confused with truncated Gaussian distribution.
In probability theory, the rectified Gaussian distribution is a modification of the Gaussian distribution when its negative elements are reset to 0 (analogous to an electronic rectifier). It is essentially a mixture of a discrete distribution (constant 0) and a continuous distribution (a truncated Gaussian distribution with interval
(0,infty)
The probability density function of a rectified Gaussian distribution, for which random variables X having this distribution, derived from the normal distribution
l{N}(\mu,\sigma2),
X\siml{N}rm{R
\Phi(x)
\delta(x)
rm{U}(x)
\mu
\mu.
Since the rectified distribution is formed by moving some of the probability mass toward the rest of the probability mass, the rectification is a mean-preserving contraction combined with a mean-changing rigid shift of the distribution, and thus the variance is decreased; therefore the variance of the rectified distribution is less than
\sigma2.
To generate values computationally, one can use
s\siml{N}(\mu,\sigma2), x=rm{max}(0,s),
x\siml{N}rm{R
A rectified Gaussian distribution is semi-conjugate to the Gaussian likelihood, and it has been recently applied to factor analysis, or particularly, (non-negative) rectified factor analysis.Harva[1] proposed a variational learning algorithm for the rectified factor model, where the factors follow a mixture of rectified Gaussian; and later Meng[2] proposed an infinite rectified factor model coupled with its Gibbs sampling solution, where the factors follow a Dirichlet process mixture of rectified Gaussian distribution, and applied it in computational biology for reconstruction of gene regulatory networks.
An extension to the rectified Gaussian distribution was proposed by Palmer et al.,[3] allowing rectification between arbitrary lower and upper bounds. For lower and upper bounds
a
b
FR(x|\mu,\sigma2)
FR(x|\mu,\sigma2)=\begin{cases} 0,&x<a,\\ \Phi(x|\mu,\sigma2),&a\lex<b,\\ 1,&x\geb,\\ \end{cases}
where
\Phi(x|\mu,\sigma2)
\mu
\sigma2
c=
| a-\mu | |
| \sigma |
, d=
| b-\mu | |
| \sigma |
.
Using the transformed constraints, the mean and variance,
\muR
| 2 | |
| \sigma | |
| R |
\mut=
| 1 | |
| \sqrt{2\pi |
| 2 | |
| \begin{align} \sigma | |
| t |
&=
| ||||||||||
| 2 |
\left(
|
\muR=\mu+\sigma\mut,
| 2 | |
| \sigma | |
| R |
=
| 2, | |
| \sigma | |
| t |
where is the error function. This distribution was used by Palmer et al. for modelling physical resource levels, such as the quantity of liquid in a vessel, which is bounded by both 0 and the capacity of the vessel.