# ⓘ Inverse-gamma distribution. In probability theory and statistics, the inverse gamma distribution is a two-parameter family of continuous probability distributio ..

## ⓘ Inverse-gamma distribution

In probability theory and statistics, the inverse gamma distribution is a two-parameter family of continuous probability distributions on the positive real line, which is the distribution of the reciprocal of a variable distributed according to the gamma distribution. Perhaps the chief use of the inverse gamma distribution is in Bayesian statistics, where the distribution arises as the marginal posterior distribution for the unknown variance of a normal distribution, if an uninformative prior is used, and as an analytically tractable conjugate prior, if an informative prior is required.

However, it is common among Bayesians to consider an alternative parametrization of the normal distribution in terms of the precision, defined as the reciprocal of the variance, which allows the gamma distribution to be used directly as a conjugate prior. Other Bayesians prefer to parametrize the inverse gamma distribution differently, as a scaled inverse chi-squared distribution.

## 1. Characterization

Moments

The n -th moment of the inverse gamma distribution is given by

E ={\frac {\beta ^{n}}{\alpha -1\cdots \alpha -n}}.}

Characteristic function

K α ⋅ {\displaystyle K_{\alpha }\cdot} in the expression of the characteristic function is the modified Bessel function of the 2nd kind.

### 1.1. Characterization Cumulative distribution function

The cumulative distribution function is the regularized gamma function

F x ; α, β = Γ α, β x Γ α = Q α, β x {\displaystyle Fx;\alpha,\beta={\frac {\Gamma \left\alpha,{\frac {\beta }{x}}\right}{\Gamma \alpha}}=Q\left\alpha,{\frac {\beta }{x}}\right\!}

where the numerator is the upper incomplete gamma function and the denominator is the gamma function. Many math packages allow direct computation of Q {\displaystyle Q}, the regularized gamma function.

### 1.2. Characterization Moments

The n -th moment of the inverse gamma distribution is given by

E ={\frac {\beta ^{n}}{\alpha -1\cdots \alpha -n}}.}

### 1.3. Characterization Characteristic function

K α ⋅ {\displaystyle K_{\alpha }\cdot} in the expression of the characteristic function is the modified Bessel function of the 2nd kind.

## 2. Properties

For α > 0 {\displaystyle \alpha > 0} and β > 0 {\displaystyle \beta > 0},

E,}

where ρ, π {\displaystyle \rho,\pi } are the pdfs of the Inverse-Gamma distributions and ρ G, π G {\displaystyle \rho _{G},\pi _{G}} are the pdfs of the Gamma distributions, Y {\displaystyle Y} is Gammaα p, β p distributed.

D K L = α p − α q ψ α p − log ⁡ Γ α p + log ⁡ Γ α q + α q log ⁡ β p − log ⁡ β q + α p β q − β p β p. {\displaystyle {\begin{aligned}D_{\mathrm {KL} }\alpha _{p},\beta _{p};\alpha _{q},\beta _{q}={}&\alpha _{p}-\alpha _{q}\psi \alpha _{p}-\log \Gamma \alpha _{p}+\log \Gamma \alpha _{q}+\alpha _{q}\log \beta _{p}-\log \beta _{q}+\alpha _{p}{\frac {\beta _{q}-\beta _{p}}{\beta _{p}}}.\end{aligned}}}

## 3. Related distributions

• If X ∼ Gamma α, β {\displaystyle X\sim {\mbox{Gamma}}\alpha,\beta\,} Gamma distribution with rate parameter β {\displaystyle \beta } then 1 X ∼ Inv-Gamma α, β {\displaystyle {\tfrac {1}{X}}\sim {\mbox{Inv-Gamma}}\alpha,\beta\,} see derivation in the next paragraph for details
• If X ∼ Inv-Gamma α, β {\displaystyle X\sim {\mbox{Inv-Gamma}}\alpha,\beta} then k X ∼ Inv-Gamma α, k β {\displaystyle kX\sim {\mbox{Inv-Gamma}}\alpha,k\beta\,}
• If X ∼ Inv-Gamma α, 1 2 {\displaystyle X\sim {\mbox{Inv-Gamma}}\alpha,{\tfrac {1}{2}}} then X ∼ Inv- χ 2 α {\displaystyle X\sim {\mbox{Inv-}}\chi ^{2}2\alpha\,} inverse-chi-squared distribution
• If X ∼ Inv-Gamma α 2, 1 2 {\displaystyle X\sim {\mbox{Inv-Gamma}}{\tfrac {\alpha }{2}},{\tfrac {1}{2}}} then X ∼ Scaled Inv- χ 2 α, 1 α {\displaystyle X\sim {\mbox{Scaled Inv-}}\chi ^{2}\alpha,{\tfrac {1}{\alpha }}\,} scaled-inverse-chi-squared distribution
• If X ∼ Inv-Gamma 1 2, c 2 {\displaystyle X\sim {\textrm {Inv-Gamma}}{\tfrac {1}{2}},{\tfrac {c}{2}}} then X ∼ Levy 0, c {\displaystyle X\sim {\textrm {Levy}}0,c\,} Levy distribution
• A multivariate generalization of the inverse-gamma distribution is the inverse-Wishart distribution.
• Inverse gamma distribution is a special case of type 5 Pearson distribution
• For the distribution of a sum of independent inverted Gamma variables see Witkovsky 2001

## 4. Derivation from Gamma distribution

Let X ∼ Gamma α, β {\displaystyle X\sim {\mbox{Gamma}}\alpha,\beta}, and recall that the pdf of the gamma distribution is

f x = β α Γ α x α − 1 e − β x {\displaystyle f_{X}x={\frac {\beta ^{\alpha }}{\Gamma \alpha}}x^{\alpha -1}e^{-\beta x}}, x > 0 {\displaystyle x> 0}.

Note that β {\displaystyle \beta } is the rate parameter from the perspective of the gamma distribution.

Define the transformation Y = g X = 1 X {\displaystyle Y=gX={\tfrac {1}{X}}}. Then, the pdf of Y {\displaystyle Y} is

f y = f X g − 1 y) | d y g − 1 y | = β α Γ α 1 y α − 1 exp ⁡ − β y 1 y 2 = β α Γ α 1 y α + 1 exp ⁡ − β y = β α Γ α y − α − 1 exp ⁡ − β y {\displaystyle {\begin{aligned}f_{Y}y&=f_{X}\leftg^{-1}y\right)\left|{\frac {d}{dy}}g^{-1}y\right|\\\end{aligned}}}

Note that β {\displaystyle \beta } is the scale parameter from the perspective of the inverse gamma distribution.

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