Generalized inverse Gaussian distribution

In probability theory and statistics, the generalized inverse Gaussian distribution is a three-parameter family of continuous probability distributions with probability density function
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 and, we can alternatively express the GIG distribution as
where is the concentration parameter while 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
where is a derivative of the modified Bessel function of the second kind with respect to the order evaluated at

Characteristic Function

The characteristic of a random variable is given as
for where 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
is a GIG with,, and. A gamma distribution of the form
is a GIG with,, and.
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, be GIG:
and let there be observed data points,, with normal likelihood function, conditioned on
where is the normal distribution, with mean and variance. Then the posterior for, given the data is also GIG:
where.

Sichel distribution

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