Parameters Probability density function Cumulative distribution function $\nu >0\!$ $x\in (0,\infty )\!$ ${\frac {2^{-\nu /2)){\Gamma (\nu /2)))\,x^{-\nu /2-1}e^{-1/(2x)}\!$ $\Gamma \!\left({\frac {\nu }{2)),{\frac {1}{2x))\right){\bigg /}\,\Gamma \!\left({\frac {\nu }{2))\right)\!$ ${\frac {1}{\nu -2))\!$ for $\nu >2\!$ $\approx {\dfrac {1}{\nu {\bigg (}1-{\dfrac {2}{9\nu )){\bigg )}^{3)))$ ${\frac {1}{\nu +2))\!$ ${\frac {2}{(\nu -2)^{2}(\nu -4)))\!$ for $\nu >4\!$ ${\frac {4}{\nu -6)){\sqrt {2(\nu -4)))\!$ for $\nu >6\!$ ${\frac {12(5\nu -22)}{(\nu -6)(\nu -8)))\!$ for $\nu >8\!$ ${\frac {\nu }{2))\!+\!\ln \!\left({\frac {\nu }{2))\Gamma \!\left({\frac {\nu }{2))\right)\right)$ $\!-\!\left(1\!+\!{\frac {\nu }{2))\right)\psi \!\left({\frac {\nu }{2))\right)$ ${\frac {2}{\Gamma ({\frac {\nu }{2)))))\left({\frac {-t}{2i))\right)^{\!\!{\frac {\nu }{4))}K_{\frac {\nu }{2))\!\left({\sqrt {-2t))\right)$ ; does not exist as real valued function ${\frac {2}{\Gamma ({\frac {\nu }{2)))))\left({\frac {-it}{2))\right)^{\!\!{\frac {\nu }{4))}K_{\frac {\nu }{2))\!\left({\sqrt {-2it))\right)$ In probability and statistics, the inverse-chi-squared distribution (or inverted-chi-square distribution) is a continuous probability distribution of a positive-valued random variable. It is closely related to the chi-squared distribution. It arises in Bayesian inference, where it can be used as the prior and posterior distribution for an unknown variance of the normal distribution.

## Definition

The inverse-chi-squared distribution (or inverted-chi-square distribution ) is the probability distribution of a random variable whose multiplicative inverse (reciprocal) has a chi-squared distribution. It is also often defined as the distribution of a random variable whose reciprocal divided by its degrees of freedom is a chi-squared distribution. That is, if $X$ has the chi-squared distribution with $\nu$ degrees of freedom, then according to the first definition, $1/X$ has the inverse-chi-squared distribution with $\nu$ degrees of freedom; while according to the second definition, $\nu /X$ has the inverse-chi-squared distribution with $\nu$ degrees of freedom. Information associated with the first definition is depicted on the right side of the page.

The first definition yields a probability density function given by

$f_{1}(x;\nu )={\frac {2^{-\nu /2)){\Gamma (\nu /2)))\,x^{-\nu /2-1}e^{-1/(2x)},$ while the second definition yields the density function

$f_{2}(x;\nu )={\frac {(\nu /2)^{\nu /2)){\Gamma (\nu /2)))x^{-\nu /2-1}e^{-\nu /(2x)}.$ In both cases, $x>0$ and $\nu$ is the degrees of freedom parameter. Further, $\Gamma$ is the gamma function. Both definitions are special cases of the scaled-inverse-chi-squared distribution. For the first definition the variance of the distribution is $\sigma ^{2}=1/\nu ,$ while for the second definition $\sigma ^{2}=1$ .

## Related distributions

• chi-squared: If $X\thicksim \chi ^{2}(\nu )$ and $Y={\frac {1}{X))$ , then $Y\thicksim {\text{Inv-))\chi ^{2}(\nu )$ • scaled-inverse chi-squared: If $X\thicksim {\text{Scale-inv-))\chi ^{2}(\nu ,1/\nu )\,$ , then $X\thicksim {\text{inv-))\chi ^{2}(\nu )$ • Inverse gamma with $\alpha ={\frac {\nu }{2))$ and $\beta ={\frac {1}{2))$ • Inverse chi-squared distribution is a special case of type 5 Pearson distribution