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Generalized inverse Gaussian distribution

Generalized inverse Gaussian
Probability density function
Probability density plots of GIG distributions
Parameters a > 0, b > 0, p real
Support x > 0
PDF
Mean

Mode
Variance
MGF
CF

In probability theory and statistics, the generalized inverse Gaussian distribution (GIG) is a three-parameter family of continuous probability distributions with probability density function

where Kp 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. It is also known as the Sichel distribution, after Herbert Sichel. Its statistical properties are discussed in Bent Jørgensen's lecture notes.

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

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


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