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{f(y;p,q)} = {\frac{\Gamma(p+q)}{\Gamma(p)\Gamma(q)}}y^{p-1}(1-y)^{q-1}, \quad 0  \end{equation}  Where the two parameters are $p$ and $q$. Changing the two parameters can alter the shape of distribution drastically, given the model a lot of flexibility.  It is easy to show that \begin{equation}  {E(y)} = \frac{p}{p+q} \equiv \mu  \end{equation}