Probability density function
Cumulative distribution function
where is the incomplete beta function|
Does not exist|
In probability theory and statistics, the beta prime distribution (also known as inverted beta distribution or beta distribution of the second kind) is an absolutely continuous probability distribution.
Beta prime distribution is defined for with two parameters α and β, having the probability density function:
where B is the Beta function.
The cumulative distribution function is
where I is the regularized incomplete beta function.
The expected value, variance, and other details of the distribution are given in the sidebox; for , the excess kurtosis is
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.
The mode of a variate X distributed as is .
Its mean is if (if the mean is infinite, in other words it has no well defined mean) and its variance is if .
For , the k-th moment is given by
For with this simplifies to
The cdf can also be written as
where is the Gauss's hypergeometric function 2F1 .
The beta prime distribution may also be reparameterized in terms of its mean μ > 0 and precision ν > 0 parameters ( 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.
Two more parameters can be added to form the generalized beta prime distribution :
- shape (real)
- scale (real)
having the probability density function:
Note that if p = q = 1 then the generalized beta prime distribution reduces to the standard beta prime distribution
Compound gamma distribution
The compound gamma distribution 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:
where G(x;a,b) is the gamma distribution with shape a and inverse scale b. This relationship can be used to generate random variables with a compound gamma, or beta prime distribution.
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.