     // intercept
  vector[K] theta;      // coefficients on Q_ast
  real&lt;lower=0&gt; sigma;  // error scale
}
model {
  y ~ normal(Q_ast * theta + alpha, sigma);  // likelihood
}
generated quantities {
  vector[K] beta;
  beta = R_ast_inverse * theta; // coefficients on x
}</code></pre>
<p>Since this Stan program generates equivalent predictions for <span class="math inline">\(y\)</span> and
the same posterior distribution for <span class="math inline">\(\alpha\)</span>, <span class="math inline">\(\bet