Parameters Probability density function Cumulative distribution function $\nu >0\,$ $\tau ^{2}>0\,$ $x\in (0,\infty )$ ${\frac {(\tau ^{2}\nu /2)^{\nu /2)){\Gamma (\nu /2)))~{\frac {\exp \left[{\frac {-\nu \tau ^{2)){2x))\right]}{x^{1+\nu /2)))$ $\Gamma \left({\frac {\nu }{2)),{\frac {\tau ^{2}\nu }{2x))\right)\left/\Gamma \left({\frac {\nu }{2))\right)\right.$ ${\frac {\nu \tau ^{2)){\nu -2))$ for $\nu >2\,$ ${\frac {\nu \tau ^{2)){\nu +2))$ ${\frac {2\nu ^{2}\tau ^{4)){(\nu -2)^{2}(\nu -4)))$ for $\nu >4\,$ ${\frac {4}{\nu -6)){\sqrt {2(\nu -4)))$ for $\nu >6\,$ ${\frac {12(5\nu -22)}{(\nu -6)(\nu -8)))$ for $\nu >8\,$ ${\frac {\nu }{2))\!+\!\ln \left({\frac {\tau ^{2}\nu }{2))\Gamma \left({\frac {\nu }{2))\right)\right)$ $\!-\!\left(1\!+\!{\frac {\nu }{2))\right)\psi \left({\frac {\nu }{2))\right)$ ${\frac {2}{\Gamma ({\frac {\nu }{2)))))\left({\frac {-\tau ^{2}\nu t}{2))\right)^{\!\!{\frac {\nu }{4))}\!\!K_{\frac {\nu }{2))\left({\sqrt {-2\tau ^{2}\nu t))\right)$ ${\frac {2}{\Gamma ({\frac {\nu }{2)))))\left({\frac {-i\tau ^{2}\nu t}{2))\right)^{\!\!{\frac {\nu }{4))}\!\!K_{\frac {\nu }{2))\left({\sqrt {-2i\tau ^{2}\nu t))\right)$ The scaled inverse chi-squared distribution is the distribution for x = 1/s2, where s2 is a sample mean of the squares of ν independent normal random variables that have mean 0 and inverse variance 1/σ2 = τ2. The distribution is therefore parametrised by the two quantities ν and τ2, referred to as the number of chi-squared degrees of freedom and the scaling parameter, respectively.

This family of scaled inverse chi-squared distributions is closely related to two other distribution families, those of the inverse-chi-squared distribution and the inverse-gamma distribution. Compared to the inverse-chi-squared distribution, the scaled distribution has an extra parameter τ2, which scales the distribution horizontally and vertically, representing the inverse-variance of the original underlying process. Also, the scaled inverse chi-squared distribution is presented as the distribution for the inverse of the mean of ν squared deviates, rather than the inverse of their sum. The two distributions thus have the relation that if

$X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,\tau ^{2})$ then   ${\frac {X}{\tau ^{2}\nu ))\sim {\mbox{inv-))\chi ^{2}(\nu )$ Compared to the inverse gamma distribution, the scaled inverse chi-squared distribution describes the same data distribution, but using a different parametrization, which may be more convenient in some circumstances. Specifically, if

$X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,\tau ^{2})$ then   $X\sim {\textrm {Inv-Gamma))\left({\frac {\nu }{2)),{\frac {\nu \tau ^{2)){2))\right)$ Either form may be used to represent the maximum entropy distribution for a fixed first inverse moment $(E(1/X))$ and first logarithmic moment $(E(\ln(X))$ .

The scaled inverse chi-squared distribution also has a particular use in Bayesian statistics, somewhat unrelated to its use as a predictive distribution for x = 1/s2. Specifically, the scaled inverse chi-squared distribution can be used as a conjugate prior for the variance parameter of a normal distribution. In this context the scaling parameter is denoted by σ02 rather than by τ2, and has a different interpretation. The application has been more usually presented using the inverse-gamma distribution formulation instead; however, some authors, following in particular Gelman et al. (1995/2004) argue that the inverse chi-squared parametrisation is more intuitive.

## Characterization

The probability density function of the scaled inverse chi-squared distribution extends over the domain $x>0$ and is

$f(x;\nu ,\tau ^{2})={\frac {(\tau ^{2}\nu /2)^{\nu /2)){\Gamma (\nu /2)))~{\frac {\exp \left[{\frac {-\nu \tau ^{2)){2x))\right]}{x^{1+\nu /2)))$ where $\nu$ is the degrees of freedom parameter and $\tau ^{2)$ is the scale parameter. The cumulative distribution function is

$F(x;\nu ,\tau ^{2})=\Gamma \left({\frac {\nu }{2)),{\frac {\tau ^{2}\nu }{2x))\right)\left/\Gamma \left({\frac {\nu }{2))\right)\right.$ $=Q\left({\frac {\nu }{2)),{\frac {\tau ^{2}\nu }{2x))\right)$ where $\Gamma (a,x)$ is the incomplete gamma function, $\Gamma (x)$ is the gamma function and $Q(a,x)$ is a regularized gamma function. The characteristic function is

$\varphi (t;\nu ,\tau ^{2})=$ ${\frac {2}{\Gamma ({\frac {\nu }{2)))))\left({\frac {-i\tau ^{2}\nu t}{2))\right)^{\!\!{\frac {\nu }{4))}\!\!K_{\frac {\nu }{2))\left({\sqrt {-2i\tau ^{2}\nu t))\right),$ where $K_{\frac {\nu }{2))(z)$ is the modified Bessel function of the second kind.

## Parameter estimation

The maximum likelihood estimate of $\tau ^{2)$ is

$\tau ^{2}=n/\sum _{i=1}^{n}{\frac {1}{x_{i))}.$ The maximum likelihood estimate of ${\frac {\nu }{2))$ can be found using Newton's method on:

$\ln \left({\frac {\nu }{2))\right)-\psi \left({\frac {\nu }{2))\right)={\frac {1}{n))\sum _{i=1}^{n}\ln \left(x_{i}\right)-\ln \left(\tau ^{2}\right),$ where $\psi (x)$ is the digamma function. An initial estimate can be found by taking the formula for mean and solving it for $\nu .$ Let ${\bar {x))={\frac {1}{n))\sum _{i=1}^{n}x_{i)$ be the sample mean. Then an initial estimate for $\nu$ is given by:

${\frac {\nu }{2))={\frac {\bar {x))((\bar {x))-\tau ^{2))}.$ ## Bayesian estimation of the variance of a normal distribution

The scaled inverse chi-squared distribution has a second important application, in the Bayesian estimation of the variance of a Normal distribution.

According to Bayes' theorem, the posterior probability distribution for quantities of interest is proportional to the product of a prior distribution for the quantities and a likelihood function:

$p(\sigma ^{2}|D,I)\propto p(\sigma ^{2}|I)\;p(D|\sigma ^{2})$ where D represents the data and I represents any initial information about σ2 that we may already have.

The simplest scenario arises if the mean μ is already known; or, alternatively, if it is the conditional distribution of σ2 that is sought, for a particular assumed value of μ.

Then the likelihood term L2|D) = p(D2) has the familiar form

${\mathcal {L))(\sigma ^{2}|D,\mu )={\frac {1}{\left({\sqrt {2\pi ))\sigma \right)^{n))}\;\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-\mu )^{2)){2\sigma ^{2))}\right]$ Combining this with the rescaling-invariant prior p(σ2|I) = 1/σ2, which can be argued (e.g. following Jeffreys) to be the least informative possible prior for σ2 in this problem, gives a combined posterior probability

$p(\sigma ^{2}|D,I,\mu )\propto {\frac {1}{\sigma ^{n+2))}\;\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-\mu )^{2)){2\sigma ^{2))}\right]$ This form can be recognised as that of a scaled inverse chi-squared distribution, with parameters ν = n and τ2 = s2 = (1/n) Σ (xi-μ)2

Gelman et al remark that the re-appearance of this distribution, previously seen in a sampling context, may seem remarkable; but given the choice of prior the "result is not surprising".

In particular, the choice of a rescaling-invariant prior for σ2 has the result that the probability for the ratio of σ2 / s2 has the same form (independent of the conditioning variable) when conditioned on s2 as when conditioned on σ2:

$p({\tfrac {\sigma ^{2)){s^{2))}|s^{2})=p({\tfrac {\sigma ^{2)){s^{2))}|\sigma ^{2})$ In the sampling-theory case, conditioned on σ2, the probability distribution for (1/s2) is a scaled inverse chi-squared distribution; and so the probability distribution for σ2 conditioned on s2, given a scale-agnostic prior, is also a scaled inverse chi-squared distribution.

### Use as an informative prior

If more is known about the possible values of σ2, a distribution from the scaled inverse chi-squared family, such as Scale-inv-χ2(n0, s02) can be a convenient form to represent a more informative prior for σ2, as if from the result of n0 previous observations (though n0 need not necessarily be a whole number):

$p(\sigma ^{2}|I^{\prime },\mu )\propto {\frac {1}{\sigma ^{n_{0}+2))}\;\exp \left[-{\frac {n_{0}s_{0}^{2)){2\sigma ^{2))}\right]$ Such a prior would lead to the posterior distribution

$p(\sigma ^{2}|D,I^{\prime },\mu )\propto {\frac {1}{\sigma ^{n+n_{0}+2))}\;\exp \left[-{\frac {ns^{2}+n_{0}s_{0}^{2)){2\sigma ^{2))}\right]$ which is itself a scaled inverse chi-squared distribution. The scaled inverse chi-squared distributions are thus a convenient conjugate prior family for σ2 estimation.

### Estimation of variance when mean is unknown

If the mean is not known, the most uninformative prior that can be taken for it is arguably the translation-invariant prior p(μ|I) ∝ const., which gives the following joint posterior distribution for μ and σ2,

{\begin{aligned}p(\mu ,\sigma ^{2}\mid D,I)&\propto {\frac {1}{\sigma ^{n+2))}\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-\mu )^{2)){2\sigma ^{2))}\right]\\&={\frac {1}{\sigma ^{n+2))}\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-{\bar {x)))^{2)){2\sigma ^{2))}\right]\exp \left[-{\frac {n(\mu -{\bar {x)))^{2)){2\sigma ^{2))}\right]\end{aligned)) The marginal posterior distribution for σ2 is obtained from the joint posterior distribution by integrating out over μ,

{\begin{aligned}p(\sigma ^{2}|D,I)\;\propto \;&{\frac {1}{\sigma ^{n+2))}\;\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-{\bar {x)))^{2)){2\sigma ^{2))}\right]\;\int _{-\infty }^{\infty }\exp \left[-{\frac {n(\mu -{\bar {x)))^{2)){2\sigma ^{2))}\right]d\mu \\=\;&{\frac {1}{\sigma ^{n+2))}\;\exp \left[-{\frac {\sum _{i}^{n}(x_{i}-{\bar {x)))^{2)){2\sigma ^{2))}\right]\;{\sqrt {2\pi \sigma ^{2}/n))\\\propto \;&(\sigma ^{2})^{-(n+1)/2}\;\exp \left[-{\frac {(n-1)s^{2)){2\sigma ^{2))}\right]\end{aligned)) This is again a scaled inverse chi-squared distribution, with parameters ${n-1}\;$ and ${s^{2}=\sum (x_{i}-{\bar {x)))^{2}/(n-1))$ .

## Related distributions

• If $X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,\tau ^{2})$ then $kX\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,k\tau ^{2})\,$ • If $X\sim {\mbox{inv-))\chi ^{2}(\nu )\,$ (Inverse-chi-squared distribution) then $X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,1/\nu )\,$ • If $X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,\tau ^{2})$ then ${\frac {X}{\tau ^{2}\nu ))\sim {\mbox{inv-))\chi ^{2}(\nu )\,$ (Inverse-chi-squared distribution)
• If $X\sim {\mbox{Scale-inv-))\chi ^{2}(\nu ,\tau ^{2})$ then $X\sim {\textrm {Inv-Gamma))\left({\frac {\nu }{2)),{\frac {\nu \tau ^{2)){2))\right)$ (Inverse-gamma distribution)
• Scaled inverse chi square distribution is a special case of type 5 Pearson distribution
• Gelman A. et al (1995), Bayesian Data Analysis, pp 474–475; also pp 47, 480
1. ^ Gelman et al (1995), Bayesian Data Analysis (1st ed), p.68