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Generalized inverse Gaussian distribution
Family of continuous probability distributions / From Wikipedia, the free encyclopedia
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In probability theory and statistics, the generalized inverse Gaussian distribution (GIG) is a three-parameter family of continuous probability distributions with probability density function
Probability density function ![]() | |||
Parameters | a > 0, b > 0, p real | ||
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Support | x > 0 | ||
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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.[1][2][3] 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.[4]