When you have a hierarchical Bayesian model (also called multilevel model), you get priors for the priors and they are called hierarchical priors. 

Consider for example:

$z = \beta_0+\beta_1{y}+\epsilon, \\ 
\epsilon \mathtt{\sim} N(0,σ)\\ 
\beta_0\mathtt{\sim} N(\alpha_0,σ_0),
\beta_1\mathtt{\sim} N(\alpha_1,σ_1),
\beta_2\mathtt{\sim} N(\alpha_2,σ_2)\\

\alpha_0\mathtt{\sim} inverse-\gamma(\alpha_{01},\theta_0)\\
$ 

In this case, you can say that, $inverse$-$\gamma$ is a hyperprior.