GENERALIZED INVERSE GAUSSIAN DISTRIBUTION

{{Probability distribution|
name =Generalized inverse Gaussian|
type =density|
pdf_image =|
cdf_image =|
parameters =a>0,b>0,p|
support =x>0|
pdf =f(x) = rac{(a/b)^{p/2}}{2 K_p(sqrt{ab})} x^{(p-1)} e^{-(ax + b/x)/2}|
cdf =|
mean = rac{sqrt{b} K_{-1-p}(sqrt{a b}) }{ sqrt{a} K_{p}(sqrt{a b})}|
median =|
mode =|
variance =|
skewness =|
kurtosis =|
entropy =|
mgf =|
char =|
}}
In probability theory, the 'generalized inverse Gaussian distribution' ('GIG') is a continuous probability distribution with probability density function
:f(x) = rac{(a/b)^{p/2}}{2 K_p(sqrt{ab})} x^{(p-1)} e^{-(ax + b/x)/2},
where ''x'' > 0, ''K''''p'' is a modified Bessel function of the third kind, ''a'' > 0, and ''b'' > 0. It is used extensively in geostatistics, statistical linguistics, finance, etc. This distribution was first proposed by Etienne Halphen[1] It was rediscovered and popularised by Ole Barndorff-Nielsen, who called it the generalized inverse Gaussian distribution, and Herbert Sichel. It is also known as the 'Sichel distribution'.

Contents
Notes

Notes


1. V. Seshadri (1997): Halphen's laws. In S. Kotz, C. B. Read and D. L. Banks (eds.): ''Encyclopedia of Statistical Sciences, Update Volume 1'', pp. 302 - 306. Wiley, New York.


This article provided by Wikipedia. To edit the contents of this article, click here for original source.

psst.. try this: add to faves