# Generalized inverse Gaussian distribution

In probability theory and statistics, the generalized inverse Gaussian distribution (GIG) is a three-parameter family of continuous probability distributions with probability density function
    $f(x)={\frac {(a/b)^{p/2}}{2K_{p}({\sqrt {ab}})}}x^{(p-1)}e^{-(ax+b/x)/2},\qquad x>0,$
where K$_p$ is a modified Bessel function of the second kind, a > 0, b > 0 and p a real parameter. It is used extensively in geostatistics, statistical linguistics, finance, etc. This distribution was first proposed by Étienne Halphen. It was rediscovered and popularised by Ole Barndorff-Nielsen, who called it the generalized inverse Gaussian distribution. Its statistical properties are discussed in Bent Jørgensen's lecture notes.

## Properties
### Alternative parametrization

By setting $\theta ={\sqrt {ab}}$ and $\eta ={\sqrt {b/a}}$, we can alternatively express the GIG distribution as
    $f(x)={\frac {1}{2\eta K_{p}(\theta )}}\left({\frac {x}{\eta }}\right)^{p-1}e^{-\theta (x/\eta +\eta /x)/2},$
where $\theta$ is the concentration parameter while $\eta$ is the scaling parameter.

### Summation

Barndorff-Nielsen and Halgreen proved that the GIG distribution is infinitely divisible.

### Entropy

The entropy of the generalized inverse Gaussian distribution is given as
    ${\begin{aligned}H={\frac {1}{2}}\log \left({\frac {b}{a}}\right)&+\log \left(2K_{p}\left({\sqrt {ab}}\right)\right)-(p-1){\frac {\left[{\frac {d}{d\nu }}K_{\nu }\left({\sqrt {ab}}\right)\right]_{\nu =p}}{K_{p}\left({\sqrt {ab}}\right)}}\\&+{\frac {\sqrt {ab}}{2K_{p}\left({\sqrt {ab}}\right)}}\left(K_{p+1}\left({\sqrt {ab}}\right)+K_{p-1}\left({\sqrt {ab}}\right)\right)\end{aligned}}$
where $\left[{\frac {d}{d\nu }}K_{\nu }\left({\sqrt {ab}}\right)\right]_{\nu =p}$ is a derivative of the modified Bessel function of the second kind with respect to the order $\nu$ evaluated at $\nu =p$

### Characteristic Function

The characteristic of a random variable $X\sim GIG(p,a,b)$ is given as(for a derivation of the characteristic function, see supplementary materials of )
    $E(e^{itX})=\left({\frac {a}{a-2it}}\right)^{\frac {p}{2}}{\frac {K_{p}\left({\sqrt {(a-2it)b}}\right)}{K_{p}\left({\sqrt {ab}}\right)}}$
for $t\in \mathbb {R}$ where $i$ denotes the imaginary number.

## Related distributions
### Special cases

The inverse Gaussian and gamma distributions are special cases of the generalized inverse Gaussian distribution for p = −1/2 and b = 0, respectively. Specifically, an inverse Gaussian distribution of the form
    $f(x;\mu ,\lambda )=\left[{\frac {\lambda }{2\pi x^{3}}}\right]^{1/2}\exp {\left({\frac {-\lambda (x-\mu )^{2}}{2\mu ^{2}x}}\right)}$
is a GIG with $a=\lambda /\mu ^{2}$, $b=\lambda$, and $p=-1/2$. A Gamma distribution of the form
    $g(x;\alpha ,\beta )=\beta ^{\alpha }{\frac {1}{\Gamma (\alpha )}}x^{\alpha -1}e^{-\beta x}$
is a GIG with $a=2\beta$, $b=0$, and $p=\alpha$.
Other special cases include the inverse-gamma distribution, for a = 0.

### Conjugate prior for Gaussian

The GIG distribution is conjugate to the normal distribution when serving as the mixing distribution in a normal variance-mean mixture. Let the prior distribution for some hidden variable, say $z$, be GIG:
    $P(z\mid a,b,p)=\operatorname {GIG} (z\mid a,b,p)$
and let there be $T$ observed data points, $X=x_{1},\ldots ,x_{T}$, with normal likelihood function, conditioned on $z:$
    $P(X\mid z,\alpha ,\beta )=\prod _{i=1}^{T}N(x_{i}\mid \alpha +\beta z,z)$
where $N(x\mid \mu ,v)$ is the normal distribution, with mean $\mu$ and variance $v$. Then the posterior for $z$, given the data is also GIG:
    $P(z\mid X,a,b,p,\alpha ,\beta )={\text{GIG}}\left(z\mid a+T\beta ^{2},b+S,p-{\frac {T}{2}}\right)$
where $\textstyle S=\sum _{i=1}^{T}(x_{i}-\alpha )^{2}$.

### Sichel distribution

The Sichel distribution results when the GIG is used as the mixing distribution for the Poisson parameter $\lambda$.