inline">\(x_n \in \mathbb{R}^D\)</span>. Each level <span class="math inline">\(l\)</span>
gets its own coefficient vector <span class="math inline">\(\beta_l \in \mathbb{R}^D\)</span>. The
hierarchical structure involves drawing the coefficients <span class="math inline">\(\beta_{l,d} \in \mathbb{R}\)</span> from a prior that is also estimated with the data. This
hierarchically estimated prior determines the amount of pooling. If
the data in each level are similar, strong pooling will be
reflected in low h