Parameters Probability density function Cumulative distribution function ${\displaystyle \alpha >0}$ shape (real)${\displaystyle \beta >0}$ shape (real) ${\displaystyle x\in [0,\infty )\!}$ ${\displaystyle f(x)={\frac {x^{\alpha -1}(1+x)^{-\alpha -\beta )){B(\alpha ,\beta )))\!}$ ${\displaystyle I_((\frac {x}{1+x))(\alpha ,\beta )))$ where ${\displaystyle I_{x}(\alpha ,\beta )}$ is the incomplete beta function ${\displaystyle {\frac {\alpha }{\beta -1)){\text{ if ))\beta >1}$ ${\displaystyle {\frac {\alpha -1}{\beta +1)){\text{ if ))\alpha \geq 1{\text{, 0 otherwise))\!}$ ${\displaystyle {\frac {\alpha (\alpha +\beta -1)}{(\beta -2)(\beta -1)^{2))}{\text{ if ))\beta >2}$ ${\displaystyle {\frac {2(2\alpha +\beta -1)}{\beta -3)){\sqrt {\frac {\beta -2}{\alpha (\alpha +\beta -1)))}{\text{ if ))\beta >3}$ Does not exist ${\displaystyle {\frac {e^{-it}\Gamma (\alpha +\beta )}{\Gamma (\beta )))G_{1,2}^{\,2,0}\!\left(\left.{\begin{matrix}\alpha +\beta \\\beta ,0\end{matrix))\;\right|\,-it\right)}$

In probability theory and statistics, the beta prime distribution (also known as inverted beta distribution or beta distribution of the second kind[1]) is an absolutely continuous probability distribution. If ${\displaystyle p\in [0,1]}$ has a beta distribution, then the odds ${\displaystyle {\frac {p}{1-p))}$ has a beta prime distribution.

## Definitions

Beta prime distribution is defined for ${\displaystyle x>0}$ with two parameters α and β, having the probability density function:

${\displaystyle f(x)={\frac {x^{\alpha -1}(1+x)^{-\alpha -\beta )){B(\alpha ,\beta )))}$

where B is the Beta function.

${\displaystyle F(x;\alpha ,\beta )=I_{\frac {x}{1+x))\left(\alpha ,\beta \right),}$

where I is the regularized incomplete beta function.

The expected value, variance, and other details of the distribution are given in the sidebox; for ${\displaystyle \beta >4}$, the excess kurtosis is

${\displaystyle \gamma _{2}=6{\frac {\alpha (\alpha +\beta -1)(5\beta -11)+(\beta -1)^{2}(\beta -2)}{\alpha (\alpha +\beta -1)(\beta -3)(\beta -4))).}$

While the related beta distribution is the conjugate prior distribution of the parameter of a Bernoulli distribution expressed as a probability, the beta prime distribution is the conjugate prior distribution of the parameter of a Bernoulli distribution expressed in odds. The distribution is a Pearson type VI distribution.[1]

The mode of a variate X distributed as ${\displaystyle \beta '(\alpha ,\beta )}$ is ${\displaystyle {\hat {X))={\frac {\alpha -1}{\beta +1))}$. Its mean is ${\displaystyle {\frac {\alpha }{\beta -1))}$ if ${\displaystyle \beta >1}$ (if ${\displaystyle \beta \leq 1}$ the mean is infinite, in other words it has no well defined mean) and its variance is ${\displaystyle {\frac {\alpha (\alpha +\beta -1)}{(\beta -2)(\beta -1)^{2))))$ if ${\displaystyle \beta >2}$.

For ${\displaystyle -\alpha , the k-th moment ${\displaystyle E[X^{k}]}$ is given by

${\displaystyle E[X^{k}]={\frac {B(\alpha +k,\beta -k)}{B(\alpha ,\beta ))).}$

For ${\displaystyle k\in \mathbb {N} }$ with ${\displaystyle k<\beta ,}$ this simplifies to

${\displaystyle E[X^{k}]=\prod _{i=1}^{k}{\frac {\alpha +i-1}{\beta -i)).}$

The cdf can also be written as

${\displaystyle {\frac {x^{\alpha }\cdot {}_{2}F_{1}(\alpha ,\alpha +\beta ,\alpha +1,-x)}{\alpha \cdot B(\alpha ,\beta )))}$

where ${\displaystyle {}_{2}F_{1))$ is the Gauss's hypergeometric function 2F1 .

### Alternative parameterization

The beta prime distribution may also be reparameterized in terms of its mean μ > 0 and precision ν > 0 parameters ([2] p. 36).

Consider the parameterization μ = α/(β-1) and ν = β- 2, i.e., α = μ( 1 + ν) and β = 2 + ν. Under this parameterization E[Y] = μ and Var[Y] = μ(1 + μ)/ν.

### Generalization

Two more parameters can be added to form the generalized beta prime distribution ${\displaystyle \beta '(\alpha ,\beta ,p,q)}$:

• ${\displaystyle p>0}$ shape (real)
• ${\displaystyle q>0}$ scale (real)

having the probability density function:

${\displaystyle f(x;\alpha ,\beta ,p,q)={\frac {p\left({\frac {x}{q))\right)^{\alpha p-1}\left(1+\left({\frac {x}{q))\right)^{p}\right)^{-\alpha -\beta )){qB(\alpha ,\beta )))}$

with mean

${\displaystyle {\frac {q\Gamma \left(\alpha +{\tfrac {1}{p))\right)\Gamma (\beta -{\tfrac {1}{p)))}{\Gamma (\alpha )\Gamma (\beta )))\quad {\text{if ))\beta p>1}$

and mode

${\displaystyle q\left({\frac {\alpha p-1}{\beta p+1))\right)^{\tfrac {1}{p))\quad {\text{if ))\alpha p\geq 1}$

Note that if p = q = 1 then the generalized beta prime distribution reduces to the standard beta prime distribution.

This generalization can be obtained via the following invertible transformation. If ${\displaystyle y\sim \beta '(\alpha ,\beta )}$ and ${\displaystyle x=qy^{1/p))$ for ${\displaystyle q,p>0}$, then ${\displaystyle x\sim \beta '(\alpha ,\beta ,p,q)}$.

#### Compound gamma distribution

The compound gamma distribution[3] is the generalization of the beta prime when the scale parameter, q is added, but where p = 1. It is so named because it is formed by compounding two gamma distributions:

${\displaystyle \beta '(x;\alpha ,\beta ,1,q)=\int _{0}^{\infty }G(x;\alpha ,r)G(r;\beta ,q)\;dr}$

where ${\displaystyle G(x;a,b)}$ is the gamma pdf with shape ${\displaystyle a}$ and inverse scale ${\displaystyle b}$.

The mode, mean and variance of the compound gamma can be obtained by multiplying the mode and mean in the above infobox by q and the variance by q2.

Another way to express the compounding is if ${\displaystyle r\sim G(\beta ,q)}$ and ${\displaystyle x\mid r\sim G(\alpha ,r)}$, then ${\displaystyle x\sim \beta '(\alpha ,\beta ,1,q)}$. (This gives one way to generate random variates with compound gamma, or beta prime distributions. Another is via the ratio of independent gamma variates, as shown below.)

## Properties

• If ${\displaystyle X\sim \beta '(\alpha ,\beta )}$ then ${\displaystyle {\tfrac {1}{X))\sim \beta '(\beta ,\alpha )}$.
• If ${\displaystyle Y\sim \beta '(\alpha ,\beta )}$, and ${\displaystyle X=qY^{1/p))$, then ${\displaystyle X\sim \beta '(\alpha ,\beta ,p,q)}$.
• If ${\displaystyle X\sim \beta '(\alpha ,\beta ,p,q)}$ then ${\displaystyle kX\sim \beta '(\alpha ,\beta ,p,kq)}$.
• ${\displaystyle \beta '(\alpha ,\beta ,1,1)=\beta '(\alpha ,\beta )}$
• If ${\displaystyle X_{1}\sim \beta '(\alpha ,\beta )}$ and ${\displaystyle X_{2}\sim \beta '(\alpha ,\beta )}$ two iid variables, then ${\displaystyle Y=X_{1}+X_{2}\sim \beta '(\gamma ,\delta )}$ with ${\displaystyle \gamma ={\frac {2\alpha (\alpha +\beta ^{2}-2\beta +2\alpha \beta -4\alpha +1)}{(\beta -1)(\alpha +\beta -1)))}$ and ${\displaystyle \delta ={\frac {2\alpha +\beta ^{2}-\beta +2\alpha \beta -4\alpha }{\alpha +\beta -1))}$, as the beta prime distribution is infinitely divisible.
• More generally, let ${\displaystyle X_{1},...,X_{n}n}$ iid variables following the same beta prime distribution, i.e. ${\displaystyle \forall i,1\leq i\leq n,X_{i}\sim \beta '(\alpha ,\beta )}$, then the sum ${\displaystyle S=X_{1}+...+X_{n}\sim \beta '(\gamma ,\delta )}$ with ${\displaystyle \gamma ={\frac {n\alpha (\alpha +\beta ^{2}-2\beta +n\alpha \beta -2n\alpha +1)}{(\beta -1)(\alpha +\beta -1)))}$ and ${\displaystyle \delta ={\frac {2\alpha +\beta ^{2}-\beta +n\alpha \beta -2n\alpha }{\alpha +\beta -1))}$.
• If ${\displaystyle X\sim F(2\alpha ,2\beta )}$ has an F-distribution, then ${\displaystyle {\tfrac {\alpha }{\beta ))X\sim \beta '(\alpha ,\beta )}$, or equivalently, ${\displaystyle X\sim \beta '(\alpha ,\beta ,1,{\tfrac {\beta }{\alpha )))}$.
• If ${\displaystyle X\sim {\textrm {Beta))(\alpha ,\beta )}$ then ${\displaystyle {\frac {X}{1-X))\sim \beta '(\alpha ,\beta )}$.
• If ${\displaystyle X\sim \beta '(\alpha ,\beta )}$ then ${\displaystyle {\frac {X}{1+X))\sim {\textrm {Beta))(\alpha ,\beta )}$.
• For gamma distribution parametrization I:
• If ${\displaystyle X_{k}\sim \Gamma (\alpha _{k},\theta _{k})}$ are independent, then ${\displaystyle {\tfrac {X_{1)){X_{2))}\sim \beta '(\alpha _{1},\alpha _{2},1,{\tfrac {\theta _{1)){\theta _{2))})}$. Note ${\displaystyle \alpha _{1},\alpha _{2},{\tfrac {\theta _{1)){\theta _{2))))$ are all scale parameters for their respective distributions.
• For gamma distribution parametrization II:
• If ${\displaystyle X_{k}\sim \Gamma (\alpha _{k},\beta _{k})}$ are independent, then ${\displaystyle {\tfrac {X_{1)){X_{2))}\sim \beta '(\alpha _{1},\alpha _{2},1,{\tfrac {\beta _{2)){\beta _{1))})}$. The ${\displaystyle \beta _{k))$ are rate parameters, while ${\displaystyle {\tfrac {\beta _{2)){\beta _{1))))$ is a scale parameter.
• If ${\displaystyle \beta _{2}\sim \Gamma (\alpha _{1},\beta _{1})}$ and ${\displaystyle X_{2}\mid \beta _{2}\sim \Gamma (\alpha _{2},\beta _{2})}$, then ${\displaystyle X_{2}\sim \beta '(\alpha _{2},\alpha _{1},1,\beta _{1})}$. The ${\displaystyle \beta _{k))$ are rate parameters for the gamma distributions, but ${\displaystyle \beta _{1))$ is the scale parameter for the beta prime.
• ${\displaystyle \beta '(p,1,a,b)={\textrm {Dagum))(p,a,b)}$ the Dagum distribution
• ${\displaystyle \beta '(1,p,a,b)={\textrm {SinghMaddala))(p,a,b)}$ the Singh–Maddala distribution.
• ${\displaystyle \beta '(1,1,\gamma ,\sigma )={\textrm {LL))(\gamma ,\sigma )}$ the log logistic distribution.
• The beta prime distribution is a special case of the type 6 Pearson distribution.
• If X has a Pareto distribution with minimum ${\displaystyle x_{m))$ and shape parameter ${\displaystyle \alpha }$, then ${\displaystyle {\dfrac {X}{x_{m))}-1\sim \beta ^{\prime }(1,\alpha )}$.
• If X has a Lomax distribution, also known as a Pareto Type II distribution, with shape parameter ${\displaystyle \alpha }$ and scale parameter ${\displaystyle \lambda }$, then ${\displaystyle {\frac {X}{\lambda ))\sim \beta ^{\prime }(1,\alpha )}$.
• If X has a standard Pareto Type IV distribution with shape parameter ${\displaystyle \alpha }$ and inequality parameter ${\displaystyle \gamma }$, then ${\displaystyle X^{\frac {1}{\gamma ))\sim \beta ^{\prime }(1,\alpha )}$, or equivalently, ${\displaystyle X\sim \beta ^{\prime }(1,\alpha ,{\tfrac {1}{\gamma )),1)}$.
• The inverted Dirichlet distribution is a generalization of the beta prime distribution.
• If ${\displaystyle X\sim \beta '(\alpha ,\beta )}$, then ${\displaystyle \ln X}$ has a generalized logistic distribution. More generally, if ${\displaystyle X\sim \beta '(\alpha ,\beta ,p,q)}$, then ${\displaystyle \ln X}$ has a scaled and shifted generalized logistic distribution.

## Notes

1. ^ a b Johnson et al (1995), p 248
2. ^ Bourguignon, M.; Santos-Neto, M.; de Castro, M. (2021). "A new regression model for positive random variables with skewed and long tail". Metron. 79: 33–55. doi:10.1007/s40300-021-00203-y. S2CID 233534544.
3. ^ Dubey, Satya D. (December 1970). "Compound gamma, beta and F distributions". Metrika. 16: 27–31. doi:10.1007/BF02613934. S2CID 123366328.

## References

• Johnson, N.L., Kotz, S., Balakrishnan, N. (1995). Continuous Univariate Distributions, Volume 2 (2nd Edition), Wiley. ISBN 0-471-58494-0
• Bourguignon, M.; Santos-Neto, M.; de Castro, M. (2021), "A new regression model for positive random variables with skewed and long tail", Metron, 79: 33–55, doi:10.1007/s40300-021-00203-y, S2CID 233534544