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Bayesian applications

The generalized Beta distribution βτ(c,d,κ) is a continuous distribution on (0,1) with density function proportional to uc1(1u)d1(1+(τ1)u)κ,u(0,1), with parameters c>0, d>0, κR and τ>0.

The (scaled) generalized Beta prime distribution βτ(c,d,κ,σ) is the distribution of the random variable σ×U1U where Uβτ(c,d,κ).

Application to the Bayesian binomial model

Assume a βτ(c,d,κ) prior distribution is assigned to the success probability parameter θ of the binomial model with n trials. Then the posterior distribution of θ after x successes have been observed is (θx)βτ(c+x,d+nx,κ).

Application to the Bayesian ‘two Poisson samples’ model

Let the statistical model given by two independent observations xP(λT),yP(μS), where S and T are known design parameters and μ and λ are the unknown parameters.

Assign the following independent prior distributions on μ and ϕ:=λμ (the relative risk): μG(a,b),ϕβ(c,d,σ), where G(a,b) is the Gamma distribution with shape parameter a and rate parameter b, and β(c,d,σ) is the scaled Beta prime distribution with shape parameters c and d and scale σ, that is the distribution of the random variable σ×U1U where Uβ(c,d).

Then the posterior distribution of ϕ is (ϕx,y)βρ/σ(c+x,a+d+y,c+d,ρ) where ρ=b+TS.